OECD Regions at a Glance 2016

OECD Regions at a Glance shows how regions and cities contribute to national economic growth and well‑being. This edition updates more than 40 region-by-region indicators to assess disparities within countries and their evolution over the past 15 years. The report covers all OECD member countries and, where data are available, Brazil, People’s Republic of China, Colombia, India, Latvia, Lithuania, Peru, the Russian Federation and South Africa. New to this edition: OECD Regions at a Glance • A comprehensive picture of well-being in the 391 OECD regions, based on 11 aspects that shape people’s lives: income, jobs, housing, education, health, environment, safety, civic engagement and governance, 2016 access to services, social connections, and life satisfaction. • Recent trends in subnational government finances and indicators on how competencies are allocated across levels of governments.

Contents Executive summary Reader’s guide 1. Well-being in regions 2. Regions as drivers of national competitiveness 3. Subnational government finance and investment for regional development 4. Inclusion and sustainability in regions Annex A. Defining regions and functional urban areas Annex B. Sources and data description Annex C. Indexes and estimation techniques Annex D. Responsibilities across levels of government

Further reading: OECD Regional Outlook 2016 (forthcoming) How’s Life in Your Region? Measuring Regional and Local Well-being for Policy Making (2014) OECD Regions at a at Regions OECD www.oecd.org/regional/regions-at-a-glance.htm www.oecdregionalwellbeing.org/

Glance 2016 Glance

Consult this publication on line at http://dx.doi.org/10.1787/reg_glance-2016-en. This work is published on the OECD iLibrary, which gathers all OECD books, periodicals and statistical databases. Visit www.oecd-ilibrary.org for more information.

isbn 978-92-64-25209-7 04 2016 03 1 p

OECD Regions at a Glance 2016 This work is published under the responsibility of the Secretary-General of the OECD. The opinions expressed and arguments employed herein do not necessarily reflect the official views of OECD member countries.

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Please cite this publication as: OECD (2016), OECD Regions at a Glance 2016, OECD Publishing, Paris. http://dx.doi.org/10.1787/reg_glance-2016-en

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Series: OECD Regions at a Glance ISSN 1999-0049 (print) ISSN 1999-0057 (online)

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Photo credits: Cover illustration: © Jeffrey Fisher. Chapter 1: © Pavel Vakhrushev/Shutterstock.com. Chapter 2: © image100/Corbis/Inmagine ltd. Chapter 3: © Sergey Nivens – Fotolia.com. Chapter 4: © Corbis/Inmagine ltd.

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Foreword

OECD Regions at a Glance shows how regions and cities contribute to national economic growth and well-being. This edition updates more than 40 region-by-region indicators to assess disparities within countries and their evolution over the past 15 years. The report covers all OECD member countries and, where data are available, Brazil, People’s Republic of China, Colombia, India, Latvia, Lithuania, Peru, the Russian Federation and South Africa. The report is organised into four chapters plus statistical annexes. The Reader’s Guide provides a description of the way OECD subnational information has developed across a range of topics and different territorial levels, including administrative and economic regions. Chapter 1 offers, for the first time, a comprehensive picture of well-being outcomes across regions, countries, and over time. This assessment is based on a multi-dimensional framework covering 11 dimensions of well-being: income, jobs, housing, health, education, access to services, safety, environment, civic engagement and governance, life satisfaction, and community. A zoom in on how each OECD region performs on the various well-being dimensions is included with interactive graphs available at www.oecdregionalwellbeing.org. Chapter 2 illustrates the regional contribution to national growth, highlights factors driving the competitive edge of regions and shows how these factors are distributed within countries. It also provides comparative analysis of the economic competitiveness and labour market trends in the 281 OECD metropolitan areas. The analysis relies on a common definition of urban areas in OECD countries, consisting of densely populated cities and their less- populated surrounding territories linked to the cities by a high level of commuting to work. A new feature in this edition, the report offers insights on the challenges subnational governments perceive for infrastructure investment and documents how financial competencies are allocated across levels of governments. Recent trends in subnational government finances complete Chapter 3. Chapter 4 looks at regional disparities on social inclusion and environmental sustainability, providing new measures of quality of life in regions and demographic changes. OECD Regions at a Glance 2016 was prepared by the Territorial Analysis and Statistics Unit of the OECD Directorate for Public Governance and Territorial Development. It has greatly benefited from comments and guidance from the Delegates of the OECD Working Party on Territorial Indicators (WPTI) and colleagues of the OECD Regional Development Policy Division. This report was supervised and edited by Monica Brezzi. Rolf Alter, Luiz De Mello and Joaquim Oliveira Martins are gratefully acknowledged for their comments on drafts of various chapters. Lead authors for each of the chapters were: Justine Boulant and Paolo Veneri (Chapter 1), Eric Gonnard and Daniel Sanchez-Serra (Chapter 2), Isabelle Chatry and Dorothée Allain-Dupré (Chapter 3), Eric Gonnard and Marcos Díaz Ramirez (Chapter 4). Karen Maguire and Johannes Weber prepared the indicators on regional innovation (Chapter 2). Eric Gonnard and Daniel Sanchez-Serra are gratefully acknowledged for providing extensive statistical support throughout the publication and preparing the maps. Gemma Nellies and Pilar Philip are kindly acknowledged for editing and preparing the report for publication. Damian Garnys and Kate Lancaster provided editorial assistance.

OECD REGIONS AT A GLANCE 2016 © OECD 2016 3

TABLE OF CONTENTS

Table of contents

Editorial: Regions and Cities – Key actors for delivery on SDGs ...... 7

Executive summary ...... 9

Reader’s guide ...... 13

Chapter 1. Well-being in regions ...... 21 The geography of well-being...... 22 Household income ...... 24 Housing conditions ...... 26 Jobs...... 28 Education ...... 30 Access to services ...... 32 Health status ...... 34 Safety ...... 36 Environment ...... 38 Civic engagement and governance ...... 40 Subjective well-being in regions...... 42

Chapter 2. Regions as drivers of national competitiveness...... 45 Population and population changes in regions ...... 46 How metropolitan areas contribute to population change...... 52 Regional contributions to GDP growth ...... 54 Regional economic disparities ...... 60 Contribution of metropolitan areas to national economies ...... 66 Regional contributions to change in employment ...... 70 Productivity growth in regions ...... 72 Where productivity gains are happening ...... 76 Regional specialisation and productivity growth ...... 78 Impact of the crisis on regional economic disparities ...... 80 Employment and unemployment in metropolitan areas ...... 82 Regional concentration of innovation related resources ...... 84 Regions and venture capital ...... 86 Regional differences in highly-skilled workers ...... 88 Regional patterns of co-patenting ...... 90 Patent activity in metropolitan areas...... 92

Chapter 3. Subnational government finance and investment for regional development ...... 95 Subnational government spending ...... 96 Subnational government spending by type ...... 98 Subnational government spending by economic function...... 100 Spending responsibilities across levels of government ...... 102

OECD REGIONS AT A GLANCE 2016 © OECD 2016 5 TABLE OF CONTENTS

Subnational government investment ...... 106 Subnational government revenue ...... 110 Subnational government debt ...... 112 Challenges for infrastructure investment at subnational level ...... 114

Chapter 4. Inclusion and sustainability in regions ...... 119 Concentration of the elderly and children in regions ...... 120 Demographic challenges of metropolitan areas ...... 122 Population mobility among regions ...... 124 Regional disparities in youth unemployment ...... 126 Part-time employment in regions ...... 128 Regional access to health ...... 130 Municipal waste ...... 132 Household income in metropolitan areas...... 134

Annex A. Defining regions and functional urban areas...... 137

Annex B. Sources and data description...... 146

Annex C. Indexes and estimation techniques ...... 174

Annex D. Responsibilities across levels of government...... 178

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6 OECD REGIONS AT A GLANCE 2016 © OECD 2016 EDITORIAL: REGIONS AND CITIES – KEY ACTORS FOR DELIVERY ON SDGS

Editorial: Regions and Cities – Key actors for delivery on SDGs

The ratification of the Sustainable Development Goals (SDGs) at the UN General Assembly in September 2015, composed of 17 goals and 169 targets, set a global agenda for achieving environmental sustainability, social inclusion and economic development by 2030. They provide a set of ambitions to whose realization all countries must contribute. One of the challenges is adjusting our focus, looking beyond national approaches to the powerful role that regions and cities play. The global agenda will require local data, the engagement of many stakeholders and all levels of government, and improved government capacity to steer and manage the delivery of public policies for inclusive growth. Regions at a Glance 2016 makes a critical contribution to advancing this global agenda, providing disaggregated data and unveiling the differences within countries that otherwise remain hidden behind national averages. For the first time, the assessment of well-being outcomes across OECD regions includes a range of dimensions, from income and jobs to health, the environment or civic engagement. It can help countries pursue policy goals that take into account the specific conditions of regions and incorporate local solutions. These new data are revealing. For example, average life expectancy at birth in Mississippi, USA, is 75 years, 6 years less than in Hawaii. Differences within some cities are even more staggering: for example, there is a 20-year gap in life expectancy between neighbourhoods in London; this is more than twice the 8-year gap among OECD countries. Similarly, while gaps across OECD regions have narrowed over the last decade in well-being dimensions such as education and access to services, gaps have increased in income, air pollution and safety. In 2014, the difference in unemployment rates among all OECD regions was above 30 percentage points – almost 10 percentage points higher than the difference in unemployment among OECD countries. The SDGs will not be achieved without the full engagement of a broad spectrum of stakeholders, including the people living in the world’s cities. Metropolitan areas, home to about half of the OECD population, are critical to the economic prosperity of countries, contributing to 62% of GDP growth of the OECD area in the period 2000-13. Household incomes were 17% higher in metropolitan areas than elsewhere in 2013. However, metropolitan areas are also host to greater inequality than their respective countries, and these inequalities grow as cities become more populated. This is not just about income: inequality encompasses many dimensions of life. In 2014, 53% of the OECD urban population was exposed to levels of air pollution higher than those recommended by the World Health Organisation. If unchecked, these disparities will grow as urbanisation continues in OECD countries. A holistic approach is required to ensure that cities are inclusive, sustainable and safe.

OECD REGIONS AT A GLANCE 2016 © OECD 2016 7 EDITORIAL: REGIONS AND CITIES – KEY ACTORS FOR DELIVERY ON SDGS

The challenge going forward is to ensure that all levels of government are implicated in the implementation of the SDGs. OECD data show that regional and local governments play crucial roles in the well-being of today’s and future generations. For example, 70% of subnational government (SNG) spending goes to education, health, economic affairs and social expenditures. At the same time, Regions at a Glance documents how spending responsibilities are shared across central and subnational governments. But aligning priorities between national and subnational governments and ensuring the capacities and resources needed for implementation remain critical challenges. New data from an OECD-EU Committee of Regions survey of European regional and local authorities show that the lack of co-ordination across sectors and levels of government, red tape, and excessive administrative procedures are the top challenges for infrastructure investment at the subnational level. The SDGs, UN Conferences on Climate Change, and the New Urban Agenda of Habitat III offer opportunities to refocus our attention on multi-level policy actions and on local data. Within this context, Regions at a Glance 2016 is an important contribution to creating pathways from the local level to meeting global goals.

Rolf Alter

Director, Public Governance and Territorial Development Directorate, OECD

8 OECD REGIONS AT A GLANCE 2016 © OECD 2016 OECD Regions at a Glance 2016 © OECD 2016

Executive summary

For policy makers and citizens alike, thinking globally increasingly requires looking hard at the many different local realities within and across countries. A thorough assessment of whether life is getting better requires a wide range of measures that are able to show not only what conditions people experience, but where they experience them. OECD data show remarkably high disparities in people’s living conditions across regions and cities: for example, there is a 20 percentage point difference among unemployment rates between regions within Italy, Spain and Turkey, comparable to the difference between the national unemployment rate of Greece and that of Norway. And life expectancy varies by 8 years among all OECD countries, but by 11 years across Canada’s provinces and by 6 years among states in Australia and in the United States. This report provides a comprehensive picture of the level of progress in OECD regions and metropolitan areas towards more inclusive and sustainable development. It does so through eleven well-being dimensions, those that shape people’s material conditions (income, jobs and housing) and their quality of life (health, education, access to services, environment, safety, civic engagement and governance, community, and life satisfaction). These dimensions are gauged through outcomes indicators, which capture improvements in people’s lives. The report also looks at what local resources are being mobilised to increase national prosperity and well-being, to better assess the contribution of regions to national performance. Since the economic crisis of 2008, many regions are still struggling to increase the productivity of firms and people and to restore employment. Traditionally, relatively few regions have led national job creation: on average, regions that concentrated 20% of OECD employment in 2000 have created one-third of the overall employment growth in the period 2000-14 and 50% or more in the Czech Republic, Estonia, Hungary, Korea and Poland. However, since 2008 employment growth has also slowed down in the most dynamic regions in all OECD countries, with the exception of Israel, Luxembourg, Mexico and Turkey. Regional and local governments (collectively known as “subnational governments” or SNGs) control many policy levers for promoting prosperity and well-being. SNGs were responsible for around 40% of total public expenditure and 60% of public investment in 2014 in the OECD area. Education, health, general public services, economic affairs and social expenditure represent the bulk of SNG expenditure (85%). At the same time, responsibilities for these sectors are often shared, requiring co-ordination across national and subnational levels of governments to ensure effective and coherent policy making. Indeed, lack of such co-ordination was indicated as a top challenge by three-quarters of European SNGs participating in an OECD-Committee of the Regions survey in 2015.

9 EXECUTIVE SUMMARY

Key findings Geography matters for well-being ● While gaps between regions have narrowed in education levels and access to services over the last decade, they have increased in income, air pollution and safety. ● Income is also unequally distributed within regions. Inequality in household disposable income in some regions in Israel, Spain, Turkey and the United States is much higher than inequality in each country as a whole. While households in OECD metropolitan areas are, on average, 17% richer in income than elsewhere, income inequalities are the highest in metropolitan areas, and the share of household income devoted to housing expenditure can be 15 percentage points higher in some metropolitan areas than in the rest of the country. ● Improvement in the educational attainment of the workforce in less-educated regions has narrowed the gaps with more-educated regions in the past 15 years. In France and Mexico, for example, thanks to improvements in the regions that had a relatively lower- educated workforce in 2000, regional disparities in the workforce with at least upper- secondary education have decreased by 12 and 7 percentage points, respectively. ● A new feature of this publication, estimates of subjective well-being at the subnational level reveal that life satisfaction and perceived social support also depend on where one lives. Forty percent of the explained variation of OECD residents’ self-reported life satisfaction is accounted for by regional characteristics, with individual characteristics accounting for the other 60%.

Regions contribute to national growth and prosperity ● In the period 2000-14, on average, GDP growth rates were lower in predominantly rural regions than predominantly urban regions in 18 out of 24 OECD countries, while job creation was higher in rural regions than in urban ones in 12 out of 24 OECD countries. ● Productivity gains explain more than 75% of growth in GDP per capita of the fastest- growing regions in the period 2000-13. A majority of these regions include large metropolitan areas where the concentration of different industries facilitates access to skilled labour, infrastructure, innovation, entrepreneurship and trade. The regions whose GDP per capita declined in the past 15 years – mainly in Greece, Italy and Spain – performed poorly both in productivity and in workforce utilisation. ● In 22 out of 27 OECD countries, lagging regions have increased the share of tertiary educated labour force faster than advanced regions, in the period 2000-13. In contrast, the share of research and development (R&D) personnel has increased faster in the advanced regions widening regional gaps in 12 out of 19 countries in the same period. ● The elderly population in OECD countries has increased more than 5 times as much as the rest of the population in the past 15 years. In 26 out of 33 OECD countries, the elderly dependency rate was higher in rural than in urban regions in 2014. Rural regions in OECD countries lost on average 11 people per 10 000 population through migration in 2011-13. At the same time, in Belgium, Korea, Portugal, Switzerland and the United Kingdom, rural regions were net recipients of domestic migration.

10 OECD REGIONS AT A GLANCE 2016 © OECD 2016 EXECUTIVE SUMMARY

Subnational governments finance and invest in regional development ● SNGs carry out 40% of total public expenditure. They have an important economic role as employers, in public procurement, and as providers of essential services in areas such as education (25% of SNG expenditure), health (17%), social protection and economic affairs (14%, for both). ● Economic affairs (mainly transport) and education are the priority investment sectors, accounting for 39% and 22% of SNG investment. SNG investment decreased by almost 4% in real terms between 2007 and 2014 in the OECD area. ● The economic crisis has led to a major deterioration of both SNG budget balance and debt in most OECD countries. In 2014, SNGs’ outstanding gross debt accounted for 24% of GDP and 20% of total public debt on average in the OECD.

OECD REGIONS AT A GLANCE 2016 © OECD 2016 11

READER’S GUIDE

Reader’s guide

The organising framework Regions at a Glance 2016 addresses two questions: ● How OECD regions perform in a wide range of well-being outcomes and what progress have regions made towards more inclusive and sustainable development, compared to the past and compared with other regions? ● Which factors drive the performance of regions, and what local resources could be better mobilised to increase national prosperity and people’s well-being? The first question is addressed in Chapter 1, which reveals the variety of regional performance, within and across countries. The framework for measuring well-being at the regional level considers a combination of individual characteristics and local conditions, to get closer to what people experience in their life. It has been conceived to improve policy coherence and effectiveness by looking at eleven dimensions, those that shape people’s material conditions (income, jobs and housing) and their quality of life (health, education, access to services, environment, safety, civic engagement and governance, community, and life satisfaction). These dimensions are gauged through indicators of “outcomes”, which capture improvements in people’s lives. For example, health is measured by the regional average life expectancy at birth, rather than public expenditure for health (input indicator) or number of doctors per population (output indicator). The well-being indicators chosen for 9 of the 11 dimensions are objective indicators that together provide a snapshot of the development of a region and, when possible, how the results are distributed among different population groups (elderly, young, women, foreign-born, etc.). For the first time in this publication two additional well-being dimensions are included, community and life satisfaction, and measured by self-reported indicators (or subjective indicators), where respondents are asked to evaluate their life or certain domains of their life. Answering the second question can inform the design of effective strategies to improve the contribution of regions to aggregate performance and can suggest policy interventions to unlock complementarities among efficiency, equity and environmental sustainability objectives. Chapters 2, 3 and 4 – “Regions as drivers for national competitiveness”, “Subnational government finance and investment for regional development”, and “Inclusion and sustainability in regions” – showcase local resources, whether human capital, infrastructure, social capital, financial means, that can be mobilised to improve well-being outcomes. Throughout the publication regional economies and societies are looked at through two lenses: the distribution of resources over space and the persistence of regional disparities over time. More precisely: ● Distribution of resources over space is assessed by looking at the proportion of a certain national variable concentrated in a limited number of regions, corresponding to 20% of

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the national population and how much these regions contribute to the national change of that variable. For example, the OECD regions with the largest gross domestic product (GDP) and corresponding to 20% of the total population generated 26% of OECD GDP in 2013. ● Regional disparities are measured by the difference between the maximum and the minimum regional values in a country (regional range), by the Gini index or by the Theil general entropy index,1 which give an indication of inequality among all regions. In Chile, Greece, Italy, Mexico, Portugal, Spain and Turkey, for example, the regional difference in the share of workforce with at least secondary education was higher than 20 percentage points in 2014.

Geographic areas utilised Traditionally, regional policy analysis has used data collected for administrative regions, that is, the regional boundaries within a country as organised by governments. Such data can provide sound evidence on the contribution of regions to national performance as well as on the persistence of disparities within a country. They show, for example, that during the past 15 years more than 30% of growth in GDP, employment and population within the OECD is attributable to a small number of regions. At the same time, the places where people live, work and socialise may have little formal relationship to the administrative boundaries around them, for example: a person may inhabit one city or region but go to work in another and, on the weekends, practice a sport in a third. Regions interact through a broad set of linkages such as job mobility, production systems, or collaboration among firms. These often cross local and regional administrative boundaries. The analysis, therefore, should take into consideration, in addition to the administrative boundaries of a region, also its economic or social area of influence known as the functional region (Figure 1). The notion of functional region can better guide the way national and city governments plan infrastructure, transportation, housing, schools, and space for culture and recreation. In summary, functional regions can trigger a change in the way policies are planned and implemented, better integrating and adapting them to the local needs.

Figure 1. Administrative and functional boundaries: Austin, Houston and Paris

Legend Legend TL3 (Economic areas) TL3 (Departments) Functional urban areas Functional urban areas of Paris Hinterland Hinterland Core area Core area Population density Population density Lower than 1 000 (inh/sq km) Lower than 1 500 (inh/sq km) Higher than 1 000 (inh/sq km) Higher than 1 500 (inh/sq km)

02040 80Kilometres 02040 80Kilometres

Note: These maps are for illustrative purposes and are without any prejudice to the status of or sovereignty over any territory covered by these maps. Source: OECD calculations based on population density as disaggregated with Corine Land Cover, Joint Research Centre for the European Environmental Agency.

14 OECD REGIONS AT A GLANCE 2016 © OECD 2016 READER’S GUIDE

This publication features data both for administrative and functional regions according to international classifications, although the availability of data for the former is much more complete than for the latter.

Territorial level classification Regions within the 34 OECD countries are classified on two territorial levels reflecting the administrative organisation of countries. The 391 OECD large (TL2) regions represent the first administrative tier of subnational government, for example, the Ontario Province in Canada. The 2 197 OECD small (TL3) regions are contained in a TL2 region. For example, the TL2 region of Aquitaine in France encompasses five TL3 regions: Dordogne, Gironde, Landes, Lot-et-Garonne and Pyrénées-Atlantiques. TL3 regions correspond to administrative regions, with the exception of Australia, Canada, Germany and the United States. All the regions are defined within national borders (See Annex A for the regional classification of each country). This classification – which, for European countries, is largely consistent with the Eurostat NUTS 2010 classification – facilitates greater comparability of geographic units at the same territorial level.2 Indeed, these two levels, which are officially established and relatively stable in all member countries, are used as a framework for implementing regional policies in most countries. Due to limited data availability, labour market indicators in Canada are presented for groups of TL3 regions. Since these groups are not part of the OECD official territorial grids, for the sake of simplicity they are labelled as non-official grids (NOGs) in this publication and compared with TL3 in the other countries. Germany has also a NOG category with the 96 Spatial Planning Regions, an intermediary level between the 16 Länders (TL2) and the 412 Kreise (TL3). The German NOG allows for a level of spatial disaggregation comparable to the other countries. For the non-OECD countries, only TL2 regions have been identified for Brazil, China, Colombia, India, Peru, the Russian Federation and South Africa, whereas for Latvia and Lithuania, TL3 are derived from the European NUTS 3.

Definition of metropolitan areas The OECD-EU definition of functional urban areas consists of highly densely populated urban centres and adjacent municipalities with high levels of commuting (travel-to-work flows) towards the densely populated municipalities. This definition overcomes previous limitations for international comparability linked to administrative boundaries. A minimum threshold for the population size of the functional urban areas is set at 50 000 population. The definition is applied to 30 OECD countries (with exception of Iceland, Israel, New Zealand and Turkey), and it identifies 1 197 urban areas of different sizes (see Figure A.5, Annex A for the detailed methodology). The aim of this approach to functional urban areas is to create a methodology that can be applied across the whole OECD, thus increasing comparability across countries, unlike definitions and methodologies created within individual countries, which have been internally focused.3 In order to establish this cross-country methodology, common thresholds and similar geographical units across countries were defined. These units and thresholds may not correspond to the ones chosen in the national definitions. Therefore, the resulting functional urban areas may differ from the ones derived from national definitions and in addition the OECD functional urban delimitation may not capture all the local factors and dynamics in the same way as national definitions.

OECD REGIONS AT A GLANCE 2016 © OECD 2016 15 READER’S GUIDE

This publication includes data on metropolitan areas, which are defined as the functional urban areas with population above 500 000. According to this methodology, there are 281 metropolitan areas in the 29 OECD countries4 corresponding to 49% of total population in 2014.

Regional typology Traditionally the OECD has classified TL3 regions as predominantly urban (PU), intermediate (IN), or predominantly rural (PR) regions. This typology is based on the percentage of regional population living in rural communities, combined with the existence of urban centres where at least one-quarter of the regional population reside. An extended regional typology has been adopted to distinguish between rural regions that are located close to larger urban centres and those that are not. The result is a four-fold classification of TL3 regions: predominantly urban (PU), intermediate regions (IN), predominantly rural regions close to a city (PRC) and predominantly rural remote regions (PRR). The distance from urban centres is measured by the driving time necessary for a certain share of the regional population to reach an urban centre with at least 50 000 people (see Annex A for a detailed description of the criteria and the resulting classification of TL3 regions). Due to a lack of information on the road network and service areas, the extended typology has not been applied to Australia, Chile and Korea. In 2014, the European Union modified the rural-urban typology, using 1 km population grids as building blocks to identify rural or urban communities, with the aim of improving international comparability; for the OECD-EU countries this rural-urban typology is presented in the publication. While the rural-urban typology is calculated only for the lower territorial level (TL3) we are also interested in characterising TL2 regions according to the distribution of population in more rural or urban areas. For this purpose, we use the share of the regional population living in functional urban areas of different population sizes located in the region. This classification has the advantage of overcoming the urban-rural split and better capturing the contiguity of urban and rural life. In this publication, a TL2 region is classified as mostly urban if more than 70% of its population lives in a functional urban area located within the TL2 region. It should be noted that, due to lack of commuting data, functional urban areas are not identified in Iceland, Israel, New Zealand and Turkey. Therefore, the classification of mostly urban TL2 regions is not applied to these four countries.

Sources of data for territorial statistics OECD Regions at a Glance 2016 includes a selection of indicators from the OECD Regional Database, the OECD Regional Well-Being Database, the OECD Metropolitan Areas Database and the OECD Subnational Government Finance Database. Most of the indicators presented in Chapters 1, 2 and 4 (TL2 and TL3 regions) come from national official sources, following internationally common methods for cross- country comparability. At the same time, regional and local data are increasingly available from a variety of sources: surveys, geo-coded data, administrative records, big data, and data produced by users. While countries have started to make use of the various sources to produce and analyse data at different geographic levels, significant methodological constraints still exist, making it a challenge to produce sound, internationally comparable statistics linked to a location. These constraints include both the varying availability of public data across OECD countries and the different standards used by National Statistical Offices in defining certain variables. Such constraints are even larger in non-OECD countries, where the production and usability of geo-coded information could be one solution to improve statistical evidence for different policy uses, such as the monitoring of

16 OECD REGIONS AT A GLANCE 2016 © OECD 2016 READER’S GUIDE

Sustainable Development Goals. The trade-off between sound methodological estimations and international comparability should be always considered, as the latter depends on the commonly available information. The indicators for the metropolitan areas presented in Chapters 1, 2 and 4 are derived by integrating different sources of data, making use of GIS and adjusting existing regional data to non-administrative boundaries. Two types of methods to obtain estimates at the desired geographical level are applied, both requiring the use of GIS tools to disaggregate socio-economic data. The first method makes use of satellite datasets (global layers) at different resolutions, which are always smaller than the considered regions. The statistics for one region are obtained by superimposing the source data onto regional boundaries. In these cases, the regional value is either the sum or a weighted average of the values observed in the source data within the (approximated) area delimited by the regional boundaries. This method has been applied, for example, to estimate air pollution (population-weighted

average of PM2.5 levels) in metropolitan areas, TL3 and TL2 regions to compensate for the lack of international standards for statistics of environmental conditions in regions. The second method makes use of GIS tools to adjust or downscale data, available only for larger geographic areas, to regularly spaced “grids” by using additional data inputs that capture how the phenomenon of interest is distributed across space. With this method, GDP, employment and unemployment have been estimated in metropolitan areas, with exception of Australia and the United States that provided economic and social statistics for the metropolitan areas (see Annex C for details on the methods to estimate indicators for metropolitan areas). GIS-based methodologies were used to estimate not only environmental, but also socio-economic indicators (GDP and labour market), because these methods are less dependent on the type of information available in the different countries and, therefore, they enable good comparability of results among metropolitan areas in different countries. This choice, however, has the disadvantages of lack of precision for some estimates and difficulty in obtaining comparable measures over time so as to monitor improvements induced by targeted policies and behavioural changes. Specific data products enabling comparison of data over time need to be produced, and, in addition, international standards for the production of indicators from remote sensing observation could be developed. The data of Chapter 3 refer to subnational governments, as classified according to the General Government Data of the OECD National Accounts. Subnational governments are defined as the sum of states (relevant only for countries having a federal or quasi-federal system of government) and local (regional and local) governments. Finally, for the first time, micro data from the Gallup World Poll were used to produce regional (TL2) estimates for three well-being indicators in Chapter 1: perception of government corruption, life satisfaction, and social support network. Survey responses over the period 2006-14 were pooled together to increase the regional sample size, and data were reweighted to better fit the age-gender cohorts in the real population.

Further resources The interactive web-based tool www.oecdregionalwellbeing.org/ allows users to measure well-being in each region, compare it against other OECD regions and monitor progress over time. Each region is assessed in 11 areas central to the quality of life: income, jobs, health, access to services, environment, education, safety, civic engagement and governnance, housing, social support network, and life satisfaction.

OECD REGIONS AT A GLANCE 2016 © OECD 2016 17 READER’S GUIDE

The different topics are visualised through interactive graphs and maps with a short comment. Regional eXplorer and Metropolitan eXplorer allow users to select from among all the indicators included in the OECD Regional and Metropolitan Areas databases and display them in different linked dynamic views such as maps, time trends, histograms, pie charts and scatter plots. The website also provides access to the data underlying the indicators and to the OECD publications on regional and local statistics. The cut-off date for data included in this publication was February 2016. Due to the time lag of subnational statistics, the last available year is generally 2014 for demographic, labour market and subnational finance data and 2013 for regional GDP, innovation statistics and social statistics. The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

Acronyms and abbreviations

Australia (TL2) TL2 regions of Australia Australia (TL3) TL3 regions of Australia COFOG Classification of the Functions of Government GDP Gross domestic product FUA Functional urban areas IN Intermediate (region) LFS Labour force survey MA Metropolitan area (functional urban area with a population of more than 500 000) NEET Adults neither employed nor in education or in training NOG Non-official grid OECD# The weighted average of the OECD regional values (# number of countries included in the average) OECD#UWA The unweighted average of the country values (# number of countries included in the average) PCT Patent Co-operation Treaty

PM2.5 Particulate matter (concentration of fine particles in the air) PPP Purchasing power parity PR Predominantly rural (region) PRC Predominantly rural (region) close to a city PRR Predominantly rural remote (region) PU Predominantly urban (region) R&D Research and development SNG Subnational government TL2 Territorial level 2 TL3 Territorial level 3 Total # countries The sum of all regions where regional data are available, including OECD and non-OECD countries

18 OECD REGIONS AT A GLANCE 2016 © OECD 2016 READER’S GUIDE

OECD Country codes

AUS Australia ISL Iceland AUT Austria ISR Israel BEL Belgium ITA Italy CAN Canada JPN Japan CHE Switzerland KOR Korea CHL Chile LUX Luxembourg CZE Czech Republic MEX Mexico DEU Germany NLD Netherlands DNK Denmark NOR Norway ESP Spain NZL New Zealand EST Estonia POL Poland FIN Finland PRT Portuga FRA France SVK Slovak Republic GBR United Kingdom SVN Slovenia GRC Greece SWE Sweden HUN Hungary TUR Turkey IRL Ireland USA United States

Other major economy codes

BRA Brazil LTU Lithuania CHN China, People’s Republic of LVA Latvia COL Colombia PER Peru IDN Indonesia RUS Russian Federation IND India ZAF South Africa

Notes 1. With the coefficient equal to 1. 2. For European countries, the Eurostat NUTS 2 and 3 classifications correspond to the OECD TL2 and 3, with the exception of Belgium, Germany and the United Kingdom where the NUTS 1 level corresponds to the OECD TL2. 3. Some OECD countries have adopted a definition for their own metropolitan areas or urban systems that looks beyond the administrative approach. For example, Australia (Australian Bureau of Statistics, 2012), Canada (Statistics Canada, 2002) and United States (U.S. Office of Management and Budget, 2000) use a functional approach similar to the one adopted here, to identify metropolitan areas. Several independent research institutions and National Statistical Offices have identified metropolitan regions in Italy, Spain, Mexico and United Kingdom based on the functional approach. 4. The functional urban area of Luxembourg has a population below 500 000 inhabitants.

OECD REGIONS AT A GLANCE 2016 © OECD 2016 19

1. WELL-BEING IN REGIONS

The geography of well-being

Household income

Housing conditions

Jobs

Education

Access to services

Health status

Safety

Environment

Civic engagement and governance

Subjective well-being in regions

The data in this chapter refer to TL2 regions in OECD and non-OECD countries, and to metropolitan areas in OECD countries. Regions are classified on two territorial levels reflecting the administrative organisation of countries. Large (TL2) regions represent the first administrative tier of subnational government. Small (TL3) regions are contained in a TL2 region. Metropolitan areas are identified on the basis of population density and commuting journeys, independently of administrative boundaries.

OECD REGIONS AT A GLANCE 2016 © OECD 2016 21 1. WELL-BEING IN REGIONS The geography of well-being

Understanding how and to what extent economic growth Part of the observed regional disparities are due to differences translates into better lives for people is important to both between countries and part to differences among regions citizens and policy makers. A crucial step in answering within a country. Around one-quarter or more of the observed these questions is having the right tools to assess people’s disparities in safety, jobs, environment, education, living conditions. While gross domestic product (GDP) has community, and GDP per capita are explained by disparities for a long time been the most used proxy for measuring within the same country (Figure 1.2). well-being, it fails to account for the actual quality of life The typology of regions, whether urban or rural, partially experienced by people. The growth of GDP per capita does explains differences in well-being outcomes. For simplicity, not always translate into better life for people. Household a region is considered mostly urban if more than 70% of its income and GDP per capita in OECD regions, for example, population lives in a functional urban area – this is a are on average positively correlated, but the same trend is definition that is consistent across countries and that does not followed everywhere. not rely on local administrative boundaries. People living in At the regional level, well-being is measured through eleven mostly urban regions have, on average, higher significant topics covering both material conditions (income, jobs, well-being outcomes in income, access to services, housing housing) and quality of life (health, education, safety, and education than those living in other areas. However, environmental quality, civic engagement, access to services, they experience significantly worse values for air pollution community, and life satisfaction). For the first time (environment) (Figure 1.3). subjective indicators are included in the regional framework to measure community and life satisfaction. Source Shifting from GDP to well-being indicators that focus on OECD (2015), OECD Regional Statistics (database), http:// people’s outcomes makes the issue of regional disparities dx.doi.org/10.1787/region-data-en. within countries broader for policy makers. A certain concentration of production in space can be beneficial for overall economic growth thanks, among other things, to Reference years and territorial level agglomeration economies. However, the spread of benefits 2003-14; TL2 (TL3 for Estonia). across all regions is an important objective for policy The classification of mostly urban regions does not include makers who want to ensure equal opportunities in Iceland, Israel, New Zealand and Turkey for lack of data on education, access to jobs and health across regions. functional urban area. Considering all OECD regions, the highest levels of regional disparities, as measured through the Theil entropy index, Further information are observed in safety (homicide rate) and income (income per capita), with disparities that have increased in both OECD (2015), How's Life? 2015: Measuring Well-being, OECD dimensions in the past decade (Figure 1.1). Publishing, Paris, http://dx.doi.org/10.1787/how_life-2015-en. OECD (2014), How's Life in Your Region?: Measuring Regional and Local Well-being for Policy Making, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264217416-en. Definition Veneri, P. and F. Murtin (2016), “Where is inclusive growth happening? Mapping multi-dimensional living standards The Theil entropy index is a measure of inequality in OECD regions”, OECD Statistics Working Papers, No. 2016/ among all regions in the OECD. The index takes on 01, OECD Publishing, Paris, http://dx.doi.org/10.1787/ values between 0 and infinity, with zero interpreted 5jm3nptzwsxq-en. as no disparity. It can be decomposed in a “within country” and “between country” component so that OECD Regional Well-Being: www.oecdregionalwellbeing.org. the sum of the two equals the total entropy. The index assigns equal weight to each region regardless of its Figure notes size; therefore differences in the values of the index 1.1-1.3: The available years may differ for the different indicators (see among countries may be partially due to differences Annex B for details). in the average size of regions in each country (see 1.3: A value higher than 100 indicates relatively better well-being Annex C for details). outcomes in mostly urban regions. To this end, the inverse of the In Figure 1.3, mostly urban regions are defined as TL2 indicators homicide, unemployment and air pollution was used regions with at least 70% of their population living in since for such indicators a higher value represents a worse situation. The difference between urban and rural regions is statistically a functional urban area located within the TL2 region significant only for the dimensions GDP per capita, income, access to (see Annex A for details). services, housing, education and environment. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

22 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

The geography of well-being

1.1. Disparities in well-being dimensions among 1.2. Per cent of disparities due to variation among TL2 regions in all OECD countries, 2003 and 2014 regions within a country, 2003 and 2014 Theil index Ratio of within component and Theil index

2014 2003 2014 2003

Safety Safety

Income Income

Jobs Jobs

Environment Environment

GDP per capita GDP per capita

Housing Housing

Access to services Access to services

Education Education

Civic engagement Civic engagement

Community Community

Life satisfaction Life satisfaction

Health Health

0 0.2 0.4 0.6 0.8 1 0 1020304050 % %

1 2 http://dx.doi.org/10.1787/888933362858 1 2 http://dx.doi.org/10.1787/888933362869

1.3. Ratio between average outcomes in mostly urban regions and outcomes in the other regions (%), 2014

% 140

130

120

110

100

1 2 http://dx.doi.org/10.1787/888933362874

OECD REGIONS AT A GLANCE 2016 © OECD 2016 23 1. WELL-BEING IN REGIONS Household income

Thedisposableincomemeasuresthecapacityof people’s material conditions have on average diverged. households (or individuals) to provide themselves with Taking into consideration the different countries, the Slovak consumable goods or services. As such, it is a better Republic, Australia, Mexico, Israel, Estonia and Chile were indicator of the material well-being of citizens than gross the countries with the highest regional disparities in 2014. domestic product (GDP) per inhabitant. Regions where net Among the countries with increasing regional disparities are commuter flows are high may display a very high GDP per the Slovak Republic, Australia, Canada and the Czech capita which does not translate into a correspondingly high Republic, while trends towards a more spatially equal income for their inhabitants. distribution of income were observed in Italy, Hungary, Disparities in regional income per capita within countries Germany, Finland and Slovenia (Figure 1.5). are generally smaller than those in terms of GDP per capita, Regional differences are not observed solely in terms of thanks to the role of public transfers. Still, in 2014 the average levels of income, but also in terms of how such per capita income in the Federal District (Mexico) and income is distributed across households living in the same Ankara (Turkey) was 3.3 times higher than in Chiapas and region. New estimations on income inequality within in Eastern Anatolia, respectively. Similarly, in the regions show that the Gini index of household disposable Slovak Republic, Australia, United States, Israel, Greece and income in some regions in the United States, Israel, Turkey Chile, inhabitants in the top income region were on average and Spain is much higher than the one in the country as a more than 50% richer than the median citizen (Figure 1.4). whole (Figure 1.6). While the regional range measures the distance between the richest and the poorest regions in a country, the coefficient Source of variation of household disposable income provides a measure of disparities among all regions. According to this OECD (2015), OECD Regional Statistics (database), http:// index, regional disparities in the levels of household dx.doi.org/10.1787/region-data-en. disposable income have increased in the last two decades in OECD (2016), “Detailed National Accounts, SNA 2008 (or SNA half of the 30 OECD countries considered, meaning that 1993): Final consumption expenditure of households”, OECD National Accounts Statistics (database), http://dx.doi.org/ 10.1787/data-00005-en. See Annex B for data sources and country-related metadata. Definition Thedisposableincomeofprivatehouseholdsis Reference years and territorial level derived from the balance of primary income by adding 1995-2014; TL2. all current transfers from the government, except social transfers in kind, and subtracting current transfers from the households such as income taxes, Further information regular taxes on wealth, regular inter-household cash transfers and social contributions. The primary OECD Regional Well-Being: www.oecdregionalwellbeing.org/. income of private households is defined as the income generated directly from market transactions, i.e. the purchase and sale of goods and services. Figure notes

Regional disposable household income is expressed 1.4-1.5: First available year: Chile, Ireland, Israel, and Slovak Republic in USD purchasing power parities (PPP) at constant 1996; United Kingdom 1997; New Zealand 1998; Slovenia 1999; prices (year 2005). Austria, Denmark, Finland, Hungary, Portugal, and Sweden 2000; Japan 2001; Estonia and Mexico 2008; Korea and Poland 2010; Norway The coefficient of variation is a measure of inequality 2011. Last available year: Mexico, Turkey and the United States 2014; among all regions of a given country (see Annex C for Australia, Austria, Canada, Czech Republic, Denmark, Estonia, the formula). It is defined as the ratio between the France, Greece, Korea, New Zealand, and United Kingdom 2013; standard deviation and the mean of a given variable. Chile, Finland, Germany, Hungary, Italy, Japan, Norway, Poland, Slovak Republic and Sweden 2012; Belgium, Israel, Netherlands, The index takes on values between 0 and infinity, Portugal and Spain 2011. with zero interpreted as no variation across regions. 1.6: Available years: Australia, Finland , Israel, Mexico, Netherlands and The index is independent of the unit in which the Norway 2014; France, Japan, New Zealand, Slovenia, Switzerland and measurement was taken. United Kingdom 2011. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

24 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

Household income

1.4. Regional variation in household disposable 1.5. Coefficient of variation of regional disposable income as a % of national average, 2014 income, 1995 and 2014

Minimum Country average Maximum 2014 1995

MEX Chiapas Federal District SVK TUR SE Anatolia Ankara AUS SVK East Slovakia Bratislava MEX AUS Tasmania Capital Territory ISR USA Mississippi District of Columbia EST ISR Northern District Tel Aviv District CHL GRC West Greece Attica ITA CHL Maule Santiago Metropolitan GRC EST Northeast North ESP Extremadura Basque Country ESP ITA Campania Bolzano-Bozen CAN GBR Northern Ireland Greater London NZL NZL Northland Wellington USA CAN Prince Edward Island Northwest Territories GBR CZE Northwest Prague CZE POL Podkarpacia Mazovia PRT PRT North Lisbon HUN FRA Nord-Pas-de-Calais Île-de-France POL HUN Northern Great Plain Central Hungary FIN NLD Groningen Utrecht DEU SWE North Middle Stockholm IRL DEU Mecklenburg-Vorpommer Bavaria SWE JPN Shikoku Southern-Kanto NLD FIN East and North Åland JPN KOR Gangwon Capital BEL NOR Hedmark and Oppland Oslo, Akershus FRA BEL Wallonia Flemish Region KOR IRL Border, Midland, West South and East SVN Eastern Slovenia Western Slovenia SVN AUT Carinthia Lower Austria NOR DNK Southern Denmark Capital AUT DNK 0 50 100 150 200 250 % 0 0.1 0.2 0.3 0.4 0.5 Coefficient of variation (regional disparities)

1 2 http://dx.doi.org/10.1787/888933362887 1 2 http://dx.doi.org/10.1787/888933362897

1.6. Regional values of Gini index of disposable household income, 2013

Country value Regional values

0.55

0.50

0.45

0.40

0.35

0.30

0.25

0.20

1 2 http://dx.doi.org/10.1787/888933362906

OECD REGIONS AT A GLANCE 2016 © OECD 2016 25 1. WELL-BEING IN REGIONS Housing conditions

Quantity of housing and its affordability are essential for On average, people in OECD countries spend just over 20% households to meet the basic need for shelter, personal of their annual household gross adjusted disposable space, and financial security. The number of rooms per income on housing. Nevertheless, housing expenditure person is a standard measure of whether people are living exceeds 35% of household disposable income in the capital in crowded conditions; across OECD regions this number regions of Oslo (Norway), Copenhagen (Denmark), varies widely, from half a room in Eastern Anatolia (Turkey) Jerusalem (Israel) and Brussels (Belgium); whereas it is to three in Vermont (United States), a difference almost below 20% in every region of Australia and Slovak Republic twice as large as that observed across OECD countries. In (Figure 1.8). 2013, regional differences in the number of rooms per person were the widest in Canada, the United States, Spain Source and Turkey (Figure 1.7). The indicator on the number of rooms per person has, however, some limitations, which OECD (2015), OECD Regional Statistics (database), http:// may hamper regional and international comparisons. First, dx.doi.org/10.1787/region-data-en. it does not take into account the possible trade-off between See Annex C for data sources and country-related metadata. the number of rooms in the dwelling and its location: some households may choose to live in smaller dwellings located Reference years and territorial level in better serviced areas than in larger homes in less 2013; TL2. desirable locations. Second, it does not take into account the overall size of accommodation, which is generally Rooms per person: no regional data are available for Chile smaller in urban areas than in rural areas. and Iceland. France, Korea and Mexico, 2010; Australia, Canada, Hungary, Italy, Portugal and United Kingdom (regional values except Scotland), 2011; Belgium, Finland, Ireland, Netherlands, Norway, Poland, Spain, Sweden, Turkey, Definition United Kingdom (national value and Scotland) and United States, 2012; and Denmark, 2014. The number of rooms per person is a measure of Housing expenditures: no regional data are available for whether people are living in crowded conditions. It is Chile, Czech Republic, France, Germany, Greece, Iceland, measured as the number of rooms in a dwelling, Korea, Mexico, Netherlands, Slovenia, Sweden and divided by the number of people living in the United States. dwelling. It excludes rooms such as a kitchenette, scullery/utility room, bathroom, toilet, garage, Ireland and Switzerland, 2010; Australia, Portugal and consulting rooms, office or shop. Spain, 2011; and Belgium, Canada, Denmark, Finland, Norway, Slovak Republic and United Kingdom, 2012. The share of household gross adjusted disposable income spent on housing and maintenance of the house as defined in the System of National Accounts Further information (SNA), includes actual and imputed rentals for OECD Regional Well-Being: www.oecdregionalwellbeing.org/. housing, expenditure on maintenance and repair of the dwelling (including miscellaneous services), on Figure notes water supply, electricity, gas and other fuels, as well as the expenditure on furniture, furnishings, 1.7: Greece, Slovak Republic and Slovenia are not depicted because the maximum and minimum values are equal; values for Greece household equipment and goods and services for correspond to NUTS 1. routine home maintenance. This measure of housing 1.8: Each observation (point) represents a TL2 region of the countries costs excludes household payments for interest and shown in the vertical axis, except the Netherlands and New Zealand principal on housing mortgages. where observations correspond to NUTS 1. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

26 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

Housing conditions

1.7. Regional variation in number of rooms per person, 2013

Minimum Country average Maximum

4 Vermont

3 Western Australia Western Saarland Hokuriku Flemish Region Flemish South Island South Newfoundland and Labrador Newfoundland and León Castile North East England East North Border, Midland and Western Midland Border, Åland Central Portugal Central Hedmark and Oppland Hedmark North Netherlands North Liguria Brittany Burgenland Småland with with Småland Islands Southern Denmark Southern

2 Switzerland Eastern Gyeongbuk Region Gyeongbuk Southwest Berlin Haifa District Haifa Southern Marmara - Marmara Southern West Federal District District Federal Southern Transdanubia Southern Mazovia Hawaii North Island North Capital Capital Vienna

1 Madeira East Netherlands East Stockholm Greater London Greater Campania Southern-Kanto Northern Territory Prague Ceuta Ceuta Nunavut Helsinki-Uusimaa Southern and Southern Eastern Ile-de-France Brussels Capital Region Capital Brussels Oslo and Akershus and Oslo Capital Region Region Capital Lake Geneva Region Geneva Lake Chiapas Lesser Poland Poland Lesser Eastern Anatolia - Anatolia Eastern East Jerusalem District 0 Plain Great Northern

1 2 http://dx.doi.org/10.1787/888933362912

1.8. Housing expenditure as a share of household disposable income, 2013 % 50

40 Akershus and Oslo Capital Capital Jerusalem District Brussels Capital Region Capital Brussels 35 Balearic Islands Balearic Åland Algarve Istanbul 30 Lake Geneva Region Geneva Lake Border, Midland, Western Midland, Border, Northern Great Plain Great Northern Silesia 25 British Columbia Vienna Hokkaido Moravia-Silesia North Island North Greater London Greater

20 Slovakia West Northern Territory

1 2 http://dx.doi.org/10.1787/888933362921

OECD REGIONS AT A GLANCE 2016 © OECD 2016 27 1. WELL-BEING IN REGIONS Jobs

Unemployment has soared in OECD countries in the years The female employment rate has increased in OECD after the economic crisis and, although it partially countries over the past decades, reaching 60% in 2014. recovered, the unemployment rate in 2014 was 7.3 in the However, important differences in the access to labour OECD area, still 1.7 points higher than in 2007. In 2014, the markets for women are still present: in 25% of OECD difference in unemployment rates among all OECD regions regions less than half of women were employed in 2014 was above 30 percentage points, almost 10 percentage suggesting that services to reconcile family and work life points higher than the difference in unemployment among and incentives to labour market participation are quite OECD countries. The largest regional disparities in diverse within countries. Regional disadvantages in female unemployment rates were found in Turkey, Spain, Italy and employment were the largest in Mexico, Turkey, Chile, the Belgium (Figure 1.9). United States, Italy and Spain (Figure 1.11). In 13 out of the 24 OECD countries considered, on average the unemployment rate has increased more in Source predominantly urban regions than in predominantly rural regions, since 2008. In Greece, Slovenia, Ireland and OECD (2015), OECD Regional Statistics (database), http:// Hungary, differences in unemployment growth by typology dx.doi.org/10.1787/region-data-en. of regions were the largest. Only in Germany and Japan See Annex for data sources and country-related metadata. unemployment decreased on average in all types of regions between 2008 and 2014 (Figure 1.10). Reference years and territorial level

Regional gender differences in employment rates: Definition Colombia, 2012; Brazil, 2013. No regional data are available for Estonia, Iceland, Israel and Luxembourg. Employed people are all persons who, during the Regional variation in unemployment rates (TL2): Brazil, reference week, worked at least one hour for pay or 2013, Czech Republic and Luxembourg 2013. No regional profit or were temporarily absent from such work. data are available for Estonia and Luxembourg. Family workers are included. The female employment rate is calculated as the ratio between female Difference in unemployment rate, by type of region 2014 employment and the female working-age population and 2008 (TL3). No regional data are available for Canada, (15-64 years). Chile, Iceland, Israel, Mexico, Norway, Switzerland and Turkey. Belgium and Portugal are excluded for lack of data Unemployed persons are defined as those who are on the years considered. without work, are available for work, and have taken active steps to find work in the last four weeks. The unemployment rate is defined as the ratio between Further information unemployed persons and labour force, where the latter is composed of unemployed and employed persons. OECD Regional Well-Being: www.oecdregionalwellbeing.org/. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

28 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

Jobs

1.9. Regional variation in the unemployment rate, 1.10. Change in unemployment rate, by type 2014 of region 2014 and 2008 (TL3)

Minimum Country average Maximum Urban Intermediate Rural

TUR Southeastern Anatolia - East NZL ESP LUX ITA Calabria Andalusia DEU BEL Brussels Capital Region SVK East Slovakia HUN CAN Nunavut JPN GRC West Greece GBR HUN Northern Great Plain DEU Berlin USA AUT Vienna AUT POL Podkarpacia KOR FRA Nord-Pas-de-Calais CZE Northwest SWE FIN Eastern and Northern Finland SVK PRT Azores FIN NLD Flevoland CHL Bío-Bío AUS MEX Federal District EST NZL Northland Region CZE USA District of Columbia FRA GBR North East England CHE Ticino POL SWE South Sweden DNK AUS Tasmania NLD SVN Eastern Slovenia ISR Jerusalem District IRL KOR Capital Region SVN JPN Kyushu, Okinawa ITA ISL Capital Region IRL Border, Midland and Western NOR South-Eastern Norway - 4- 2 0 2 4 6 8 DNK Capital Republic of Ingushetia ESP RUS Free State ZAF GRC BRA Amapá COL Quindio LVA PER Prov. const. del Callao LTU 0 10 20 30 40 0 5 10 15 20 25 % % 1 2 http://dx.doi.org/10.1787/888933362930 1 2 http://dx.doi.org/10.1787/888933362946

1.11. Regional variation in gender differences of employment rate (male-female), 2014

Minimum Maximum Country average

% 60

-1 -5 0

1 2 http://dx.doi.org/10.1787/888933362954

OECD REGIONS AT A GLANCE 2016 © OECD 2016 29 1. WELL-BEING IN REGIONS Education

Educational outcomes are some of the most influential disparities is mainly related to the high increase in determinants of current and future well-being. Evidence educational attainment in the regions originally showing shows that highly educated individuals are more likely to the lowest values. have better health and higher earnings than the less well educated. From an aggregate perspective, a well-educated workforce is also crucial for raising productivity, ensuring resiliency and adaptability to the changing needs of the labour market but also for making use of innovation. Both Definition the capacity to generate and absorb innovation are affected Upper secondary education includes high schools, by the quality of the human capital, which in turn is often lyceums, vocational schools and preparatory school enhanced by the education levels of the workforce. programmes (ISCED 3 and 4). Large educational variations can be observed across regions. In seven OECD countries the difference between the region with the highest value and that with the lowest value in the share of the workforce with at least upper secondary education is higher than 20 percentage points Source (Figure 1.12). In Turkey and Mexico, the same indicator in the two capital regions, Ankara and the Federal District, is OECD (2015), OECD Regional Statistics (database), http:// over 30 percentage points higher than that in North- dx.doi.org/10.1787/region-data-en. Eastern Anatolia (Turkey) and the state of Chiapas (Mexico) See Annex B for data sources and country-related metadata. respectively. Among non-OECD countries, Brazil, Colombia and Russia also show large disparities in the proportion of people who have completed at least upper secondary Reference years and territorial level education, ranging from 15 to 37 percentage points between the capital regions scoring at the highest and 2014; TL2 (TL3 for Estonia). some of the provincial regions scoring at the lowest levels. Within countries, regional differences in the educational Further information attainment of the workforce have changed remarkably since 2000 (Figure 1.13). In most OECD countries, such OECD (2015), Education at a Glance 2015: OECD Indicators, difference has decreased, thanks to the improvements in OECD Publishing, Paris, http://dx.doi.org/10.1787/eag- the regions with relatively lower educated workforce. 2015-en. France and Mexico have experienced the largest decreases, OECD Regional Well-Being: www.oecdregionalwellbeing.org/. respectively showing a 12 and 7 percentage points disparity across regions. However, on the other hand, other countries have witnessed an increase in regional differences. For Figure notes example, in Portugal and Belgium, the differences between the highest and the lowest proportion of the workforce 1.12: Available years: Brazil, Canada, Estonia, Israel and United States with at least upper secondary education increased by 11 2013; Iceland and New Zealand 2012; Japan and Mexico 2010; for all the other countries the last year available is 2014. and 4 percentage points respectively, as the better 1.13: First year available: Slovenia and Switzerland 2001; Iceland 2003; performing regions were able to continue increasing their Brazil 2004; Colombia and Finland 2005; Turkey 2006; Denmark 2007; share of highly educated individuals. Across the non-OECD Australia and Chile 2010; for all the other countries the first year countries considered, the share of the workforce with at available is 2000. Last year available: Brazil, Canada, Estonia, Israel least upper secondary education also increased and United States 2013; Iceland and New Zealand 2012; Japan and everywhere. In the cases of Colombia and the Russian Mexico 2010. Federation more specifically, the lowering of regional Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

30 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

Education

1.12. Regional variation in the workforce with at least secondary education, 2014

Minimum Country average Maximum % 140

120 City of Moscow Prague Bratislava Region Bratislava Silesia Thuringia Maine North Estonia North Southern-Kanto Central Hungary Central

100 British Columbia Capital Territory Central District Western Slovenia Western Antofagasta Carinthia Eastern and Northern and Eastern Capital Region Capital Brittany Upper Norrland Upper Greater London Greater Oslo and Akershus and Oslo Northwestern Switzerland Northwestern Attica Flemish Region Flemish Southern and Southern Eastern Capital Utrecht Wellington Region Wellington Capital Region Capital Lazio 80 Madrid Distrito Federal Distrito Lisbon Ankara Tohoku Northwest Federal District Federal Bremen Åland California

60 District Capital Bogotá Vorarlberg Central Slovakia Central Picardy Zeeland Warmian-Masuria Central Estonia Central Eastern Slovenia Eastern Maule Tasmania Northern District Northern Northern Norway Northern

40 Ireland Northern Prince Edward Island Edward Prince Northern Great Plain Great Northern Lake Geneva Region Geneva Lake Småland with with Småland Islands Gangwon Region Gangwon Sardinia Southern Denmark Southern Other Regions Other Jewish Autonomous Oblast Autonomous Jewish Northland Region Northland 20 Region Capital Brussels Extremadura Piauí Border, Midland and Western Midland Border, Azores Chiapas NE Anatolia - NE Anatolia East Caquetá 0 - Macedonia East Thrace

1 2 http://dx.doi.org/10.1787/888933362966

1.13. Regional difference between the highest and lowest % of the workforce with at least secondary education, 2000 and 2014

2014 2000

% 40 60

1 2 http://dx.doi.org/10.1787/888933362979

OECD REGIONS AT A GLANCE 2016 © OECD 2016 31 1. WELL-BEING IN REGIONS Access to services

Access to services affects how people obtain what is needs is comparable to that of Austria, the country with the necessary to satisfy their needs and wants. The first best performance in this area. On the other hand, the indicator used to measure access to services is the share of region of Arica Y Parinacota (Chile) has a value (21%) close households with a broadband connection, which is to that of Mexico, the country with the second highest available for all OECD regions. A broadband connection is proportion of individuals with unmet medical needs an important requirement for having access to information (Figure 1.15). and to other services that shape people’s quality of life and affect their opportunities to prosper. Source The largest regional disparities in broadband connection are observed in the countries where the average national level of OECD (2015), OECD Regional Statistics (database), http:// access to services is relatively low, such as in Turkey, Mexico dx.doi.org/10.1787/region-data-en. and Chile. In these three countries, the value in the region See Annex B for data sources and country-related metadata. with the highest proportion of households with broadband connection is more than three times higher than the lowest value. An urban-rural divide might partly explain these Reference years and territorial level regional differences. Mostly urban regions, where more than Broadband access: 2014; TL2. half of the population live in a functional urban area, show, on average, a significantly higher share of broadband The classification of mostly urban regions does not include connection than the other less urbanised regions (on Israel, New Zealand and Turkey for lack of data on average, 72% and 64%, respectively). Korea and the functional urban area. A t-test was performed to assess the Netherlands are the two countries with the highest average statistical significance of the difference in the mean proportion of households with broadband connection and average access to broadband by type of region. very low differences across regions (Figure 1.14). Unmet medical needs: 2013, except for New Zealand (2012); Another indicator relates to access to healthcare, measured TL2. Regional data were available for Austria, Chile, with self-reporied unmet medical needs. Strong regional Czech Republic, Estonia, Finland, France, Greece, Italy, variation can be observed, although this indicator is Mexico, New Zealand, Spain and the United Kingdom. currently available only for a sub-set of OECD countries at the TL2 regional level. The highest regional disparities are Further information observed in Chile, Mexico and Italy. In Magallanes y Antartica (Chile), the share of people with unmet medical Brezzi, M. and P. Luongo, “Regional Disparities In Access To Health Care: A Multilevel Analysis In Selected OECD Countries”, OECD Regional Development Working Papers, No. 2016/04, OECD Publishing, Paris, http://dx.doi.org/ 10.1787/5jm0tn1s035c-en. Definitions OECD (2015), Health at a Glance 2015: OECD Indicators, OECD The broad dimension of “access to services” can be Publishing, Paris, http://dx.doi.org/10.1787/health_glance- broken down into several domains, such as the ease 2015-en. of access to the place where a specific service is OECD (2014), How’s Life in Your Region?: Measuring Regional provided (physical accessibility), its affordability and Local Well-being for Policy Making, OECD Publishing, (economic accessibility) and the extent to which the Paris, http://dx.doi.org/10.1787/9789264217416-en. access is favoured or constrained by norms, values OECD Regional Well-Being: www.oecdregionalwellbeing.org/. and laws (institutional accessibility). The proportion of the population who experienced Figure notes unmet medical needs is defined as the individuals who report one or more occasions in which they were 1.14: Available years: Australia, Israel and Turkey 2013; Canada, Chile, in need of medical treatments or examination, but Iceland and New Zealand 2012; Japan and the United States 2011. failed to receive either. 1.14-1.15: Each observation (dot) represents a TL2 region. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

32 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

Access to services

1.14. Regional variation in the % of households with broadband connection, 2014

Minimum Country average Maximum 120 % Region Capital Zeeland Akershus Oslo and Zurich 100 Capital Region Greater London Hamburg West Sweden Helsinki-Uusimaa Capital Prague British Columbia British Flemish Region Ile-de-France Salzburg Madrid Austr. Capital Territory Capital Austr. Southern-Kanto Central Hungary SouthernEastern and Auckland Region Washington Pomerania Western Slovenia Tel Aviv District Aviv Tel East Slovakia East 80 Province of Bolzano-Bozen Lisbon Flevoland Istanbul Gangwon Region Scotland Other Regions Ticino Nuevo Leon Antofagasta 60 Wallonia Carinthia Brandenburg Tasmania Northern Jutland New Brunswick Northwest Lubusz Central Slovakia Calabria Eastern Slovenia North MiddleSweden Hedmark and Oppland Eastern and Northern Finland Extremadura 40 Shikoku Alentejo Lower Normandy Northern Hungary Mississippi Northland Region Border, Midland and Western Jerusalem District Jerusalem 20 Eastern Anatolia - East Maule 0 Chiapas

1 2 http://dx.doi.org/10.1787/888933362982

1.15. Regional variation in the % of population with unmet medical needs, 2013

Minimum Country average Maximum % 40

30 Island South San Luis San Luis Potosi

25 Arica Y Parinacota Y Arica Eastern Anatolia Eastern

20 Campania Athens 15 Island North Castile-La Mancha Castile-La Estonia 10 Nord-Pas-de-Calais Southwest Helsinki-Uusimaa Greater London Greater Vienna 5 Nuevo Leon Nuevo

0 Western Anatolia Western Wales -5 Upper Normandy Silesia Antártica Trento Moravia- Northern Greece Northern Salzburg Catalonia Magallanes y Magallanes Southern Finland Southern Province of Province -10

1 2 http://dx.doi.org/10.1787/888933362995

OECD REGIONS AT A GLANCE 2016 © OECD 2016 33 1. WELL-BEING IN REGIONS Health status

Being in good health is an important determinant of The mortality rate is also a common indicator of a quality of life and also contributes to other well-being population’s health status. When comparing values across dimensions, such as being able to pursue education, have a countries and regions, mortality rates are adjusted for age job, and participate in the activities that people value. In to remove differences solely due to a population’s age 55% of OECD regions life expectancy at birth, a common profile. Regional differences in age-adjusted mortality rates measure of health outcomes, now exceeds 80 years. The within countries were the widest in New Zealand, Canada, lowest levels of life expectancy, below 75 years, are found in United States, Portugal and Australia (Figure 1.18). In 2013, 30 regions. The difference in life expectancy among OECD the age-adjusted mortality rate in Gisborne (New Zealand), countriesis8years(betweenJapanandMexico).Within Nunavut (Canada), Mississippi (United States), Azores countries, it is 11 years between British Columbia and (Portugal) and the Northern Territory (Australia) was at Nunavut in Canada, and 6 years between the Capital least 40% higher than their country averages. Territory and the Northern Territory in Australia, or Hawaii and Mississipi in the United States (Figure 1.16). Source On average, women live longer than men in every OECD region; a woman can expect to live almost six years longer OECD (2015), OECD Regional Statistics (database), http:// than a man. These differences are the largest in Aysén dx.doi.org/10.1787/region-data-en. (Chile), Lodzkie (Poland) and Chihuahua (Mexico). In non- See Annex B for data sources and country-related OECD regions like Nenets Okrug (Russia) and Group metadata. United States: Life Expectancy, Measure of Amazon (Colombia), women live for more than 15 and America 2010-2011, www.measureofamerica.org. 10 additional years, respectively (Figure 1.17).

Reference years and territorial level

Definition 2013; TL2. Estonia TL3. Life expectancy: no regional data are available for Iceland. Life expectancy at birth measures the number of years a new born can expect to live, if death rates in each age Japan and United States, 2010; Canada, 2011; Chile, 2012. group stay the same during her or his lifetime. Mortality rates: Australia, Chile and Mexico, 2012. Age-adjusted mortality rates eliminate the difference in mortality rates due to a population’s age profile Further information and are comparable across countries and regions. Age-adjusted mortality rates are calculated by OECD Regional Well-Being: www.oecdregionalwellbeing.org/. applying the age-specific death rates of one region to the age distribution of a standard population. In this case the standard population is the population Figure notes grouped into five year age brackets, averaged over all 1.17: Each observation (point) represents a TL2 region of the countries OECD regions. shown in the vertical axis, TL3 region in Estonia and Latvia. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

34 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

Health status

1.16. Difference in life expectancy at birth among regions and country life expectancy (years), 2013

CAN OECD AUS USA EST ESP TUR PRT MEX FRA NZL CZE POL GBR ITA HUN DEU GRC BEL CHL KOR SVN AUT ISR FIN SWE SVK NLD NOR CHE DNK JPN IRL RUS COL PER 1012141618 02468 60 65 70 75 80 85 Regional difference in life expectancy at birth (years) Life expectancy at birth, country average (years)

1 2 http://dx.doi.org/10.1787/888933363005

1.17. Regional gender differences 1.18. Regional variation in age-adjusted in life expectancy at birth (female-male), 2013 mortality rates, 2013

CHL Aysén POL Lodzkie Minimum Country average Maximum MEX Chihuahua PRT Azores NZL Auckland Region Gisborne Region KOR Jeju CAN British Columbia Nunavut HUN Northern Hungary USA California Mississippi Eastern Black Sea PRT Lisbon Azores TUR AUS Victoria Northern Territory SVK Central Slovakia MEX Nayarit Federal District FRA Nord-Pas-de-Calais CZE Prague Northwest USA District of Columbia CHL Tarapacá Aysén JPN Tohoku ESP Madrid Ceuta FRA Île-de-France Nord-Pas-de-Calais ESP Basque Country TUR Eastern Black Sea Eastern Marmara - South FIN Eastern and Northern Finland POL Podkarpacia Lodzkie SVN Eastern Slovenia EST North Estonia Northeast Estonia CZE Central Moravia HUN Central Hungary Northern Hungary Saxony-Anhalt GBR Greater London Scotland DEU DEU Baden-Württemberg Saxony-Anhalt GRC North Aegean ITA Bolzano-Bozen Campania ITA Sardinia BEL Flemish Region Wallonia BEL Wallonia SVN Western Slovenia Eastern Slovenia Ticino AUT Tyrol Vienna CHE GRC Epirus East Macedonia - Thrace AUT Lower Austria SVK Bratislava Region Central Slovakia CAN Nunavut SWE Stockholm Central Norrland NOR Northern Norway ISR Jerusalem District Southern District ISR Southern District NOR Western Norway Hedmark and Oppland Zeeland NLD Utrecht Groningen NLD KOR Jeju Gyeongnam Region AUS Queensland DNK Central Jutland Zealand DEN Zealand CHE Ticino Espace Mittelland IRL Border, Midland and Western FIN Åland Eastern and Northern Finland Northern Ireland JPN Southern-Kanto Tohoku GBR ISL Capital Region Other Regions NZL Northland Region IRL Border, Midland, Western Southern and Eastern SWE South Sweden RUS Republic of Ingushetia Huancavelica Chukotka Okrug RUS PER Moquegua Nenets Okrug COL La Guajira Meta COL Group Amazon BRA Distrito Federal Roraima PER Pasco LVA Pieriga Latgale 246810121416 0 5 10 15 20 25 percentage points % 1 2 http://dx.doi.org/10.1787/888933363015 1 2 http://dx.doi.org/10.1787/888933363027

OECD REGIONS AT A GLANCE 2016 © OECD 2016 35 1. WELL-BEING IN REGIONS Safety

Personal safety is a critical dimension of regional well- social cohesion in a region. Finally, since this type of crime is being. Crime in fact has not only a direct impact on the commonly reported for insurances claims, it also overcomes victims and their families, but also on those who are not common issues of bias of statistics on property crimes due victims but live in the same community, as shown by the to different regional propensity to report the crime. increasing feelings of insecurity and low trust in the In 2014, the OECD countries showing the largest regional capacity of national and local institutions to handle the disparities for car thefts were Mexico, New Zealand, Italy safety issue (OECD, 2015). Safety is often connected with and France (Figure 1.20). In Baja California (Mexico), Ceuta other well-being outcomes such as education, health and (Spain) and Bratislava (Slovak Republic), the rate of car theft jobs. Consequently, policies pursuing better safety often was at least three times higher than the national average. build on the complementarities with the other dimensions Among the non-OECD countries, in the region Ucayali (OECD, 2014). (Peru) the rate of car theft was eight times higher than for Mexico has the highest homicide rate as well as the highest the country as a whole, and in Sakhalin Oblast (Russian regional variation among OECD countries. In 2013, the state Federation) almost four times higher (Figure 1.20). of Guerrero (Mexico) had almost 65 homicides per 100 000 inhabitants, while in Yucatan (Mexico) there were Source 2.4 homicides per 100 000 inhabitants (Figure 1.19). Large regional differences in homicides rates are also observed in OECD (2015), OECD Regional Statistics (database), http:// the United States and Canada, the regional difference is dx.doi.org/10.1787/region-data-en. around 15 homicides per 100 000 inhabitants, due to the See Annex B for data sources and country-related metadata. high incidence of homicides compared to the rest of the country in the District of Columbia and Nunavut, Reference years and territorial level respectively. Among the countries with the lowest homicide rates are Austria (0.6), Iceland (0.3) and Spain (0.6), where 2014; TL2. differences between top and bottom regions are on average Homicides: No regional data are available for Luxembourg. also relatively low. Car thefts: No regional data are available for Estonia, The theft of private property has a negative effect on Germany, Greece, Iceland, Korea, Luxembourg, the people’s well-being. It reduces household wealth, increases Netherlands, Norway and the United Kingdom. the costs associated with robbery prevention, and increases people’s perception of insecurity. Moreover, high levels of Further information private property theft might be an indirect measure of low OECD (2015), Measuring Well-being in Mexican States, OECD Publishing, Paris, http://dx.doi.org/10.1787/ 9789264246072-en. OECD (2014), How’s Life in Your Region?: Measuring Regional Definition and Local Well-being for Policy Making, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264217416-en. Homicide is the unlawful killing of a human being OECD Regional Well-Being: www.oecdregionalwellbeing.org/. with malice aforethought, more explicitly intentional murder. Reported homicides are the number of homicides reported to the police. The homicide Figure notes rate is the number of reported homicides per 1.19: 2014 data for Mexico, 2012 data for Canada, Chile, France, Iceland, 100 000 inhabitants. Netherlands and Slovenia; 2011 data for Poland; 2010 data for Germany. Motor vehicle theft is defined as the theft or attempted theft of a motor vehicle. A motor vehicle is 1.20: Japan, New Zealand and Sweden 2014; France and Slovenia 2012; Canada, Ireland, Mexico and Poland 2011. Each observation (point) a self-propelled vehicle that runs on land surfaces represents a TL2 region of the countries shown in the horizontal and not on rails. axis. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

36 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

Safety

1.19. Regional variation in homicides per 100 000 inhabitants, 2013

Minimum Country average Maximum

12 65 16 14 68 38 11 10

9 Sicily Arauca Guerrero

8 Nunavut Corsica Northeast Estonia Northeast Northern Territory 7 Republic Tyva Aysén 6 Jeju 5 DistrictColumbia of Northeastern Anatolia - Anatolia Northeastern East Central Greece Central Vaupés 4 Algarve Lubusz Brussels Capital Region Capital Brussels Central Moravia Ceuta Southern Transdanubia Southern Border, Midland and Western Midland Border, Gisborne Region Gisborne Southern District Southern

3 Slovenia Western Bratislava Region Bratislava Saarland Western Norway Western Greater London Greater Netherlands West West Sweden West Kansai region Kansai Vienna

2 Switzerland Central Central Jutland Central Eastern and Northern Finland and Northern Eastern 1 Region Capital

0 Yucatan Iowa - 1 City of Moscow Wales Azores Styria Tohoku Åland Ticino Germany Yukon

- 2 Greece Northern Southwest Atacama Jeolla Region Jeolla La Rioja Eastern Slovenia Eastern Flemish Region Flemish Podkarpacia Trøndelag West Estonia West

- 3 Slovakia Central Aosta Valley Aosta Jerusalem District North Netherlands North Other Regions Other Jutland Northern Southern and Southern Eastern

- 4 Normandy Lower Central Anatolia - Anatolia Central East Capital Territory Northern Great Plain Great Northern Småland with with Småland Islands - 5 Region Southland

1 2 http://dx.doi.org/10.1787/888933363032

1.20. Regional range in reported car thefts per 10 000 inhabitants, 2013

Country average Regional value 800

700

600 Lazio Auckland Region District Columbia of Baja California Norte Ceuta 500 Territories Northwest

400 Provence-Alpes-Côte d'Azur Western Australia Western Central District Central Antofagasta Prague

300 Southern and Eastern Zealand Stockholm Lisbon Lake Geneva RegionLake Geneva Brussels Capital Region

200 Southern Finland Central Hungary Bratislava Region Vienna Sakhalin Oblast Kansai region Southeastern Anatolia-West

100 Lodzkie Western Slovenia

ITA IRL FIN MEX NZL FRA USA ESP CAN CHL CZE ISR AUS PRT HUN CHE SVK SWE AUT BEL TUR JPN DNK POL SVN RUS PER 1 2 http://dx.doi.org/10.1787/888933363043

OECD REGIONS AT A GLANCE 2016 © OECD 2016 37 1. WELL-BEING IN REGIONS Environment

Air pollution at the national and local level is an important Deaths due to diseases associated to respiratory problems determinant of the individual well-being in regions and are frequent in some regions in southern European cities in particular due to its negative impact on health and countries such as Portugal, Spain and Greece, but also in the economy. Poland and Germany (above 100 deaths per 100 000 people). On the other hand, regions in Sweden, Slovenia and Fine particulate matters (PM2.5) are generally emitted from the combustion of liquid and solid fuels for industrial and Finland accounted for less than 60 deaths per 100 000 housing energy production, vehicles and biomass burning people due to respiratory diseases (Figure 1.23). in agriculture. The exposure to air pollution in regions and cities is greatly associated with the industry located in the territory, its level of urbanisation and the transportation Source system developed in the territory. Eurostat (2015), Deaths from diseases of the respiratory In 2014, in 52% of the OECD regions people were on average system, Eurostat Statistics Explained, http://ec.europa.eu/ exposed to levels of air pollution higher than those eurostat/statistics-explained/index.php/. recommended by the World Health Organisation (pollution OECD (2015), OECD Regional Statistics (database), http:// concentration level of 10 g/m3). In the regions of Lombardy dx.doi.org/10.1787/region-data-en. (Italy) and the Capital Region (Korea) pollution levels were 3 OECD (2015), “Metropolitan areas”, OECD Regional above 25 g/m of PM2.5 per person, the highest levels among OECD regions (Figure 1.21). People in all regions in Statistics (database), http://dx.doi.org/10.1787/data-00531-en. Canada, Sweden, Portugal, Finland, Norway, Austria, New See Annex B and C for data sources, methodology and Zealand, Iceland, Ireland and Estonia were exposed to low country-related metadata. levels of air pollution (below 10 g/m3). Air pollution levels varied greatly from region to region. The largest differences are observed in Mexico, Italy and Reference years and territorial level Chile. On the contrary, countries such as New Zealand, Iceland and Ireland present the smallest differences across 2014 (three year average 2012-14); TL2. regions (Figure 1.21). Functional urban areas (FUAs) have not been identified in Air pollution is often an issue in metropolitan areas. In 2014, Iceland, Israel, New Zealand and Turkey. The FUA of 53% of the urban population was exposed to levels of air Luxembourg does not appear in the figures since it has a pollution higher than 10 g/m3. In the Netherlands, Poland, population below 500 000. Germany, Belgium, Slovenia, Austria, Czech Republic, Hungary, Korea and the Slovak Republic more than 90% of the urban population was exposed to high pollution Further information concentration levels. On the other hand, all urban Brezzi, M. and D. Sanchez-Serra (2014), “Breathing the population in countries such as Australia, Estonia, Ireland Same Air? Measuring Air Pollution in Cities and and Norway are exposed to pollution levels well within the Regions”, OECD Regional Development Working Papers, recommended safe levels (Figure 1.22). No. 2014/11, OECD Publishing, Paris, http://dx.doi.org/ 10.1787/5jxrb7rkxf21-en. OECD (2015), Environment at a Glance 2015: OECD Indicators, Definition OECD Publishing, Paris, http://dx.doi.org/10.1787/ Particulate matter (PM), refers to a complex mixture 9789264235199-en. of sulphates, nitrates, ammonia, sodium chloride, OECD Regional Well-Being: www.oecdregionalwellbeing.org. carbon, mineral dust and water suspended in the air. WHO (2013), Health Effects of Particulate Matter: Policy Particles can be classified27 in two categories implications for countries in Eastern Europe, Caucasus and according to their origin (WHO, 2013). On the one Central Asia, www.euro.who.int/__data/assets/pdf_file/ hand, primary PM is emitted from the combustion of 0006/189051/Health-effects-of-particulate-matter-final- liquid and solid fuels for industrial and housing Eng.pdf. energy production as well as from the erosion of the pavement of the roads. On the other hand, secondary PM are the result of chemical reactions between Figure notes gaseous pollutants.

Death rates due to diseases of the respiratory system 1.21: Population-weighted exposure to PM2.5 concentration, micrograms describes mortality rates in relation to the total per cubic metre, averaged 2012-14. population due to lung diseases. 1.22: Percentage of the population, mean annual exposure, 2012-14. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

38 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

Environment

1.21. Regional variation of annual exposure to air pollution, 2014

Minimum Country average Maximum g/m3 40 120

100 Hebei

30 Capital Region Capital Lombardia 80 Morelos Moravskoslezsko

25 Siirt Sirnak, Batman, Mardin, a de Melilla Southern District Southern Kärnten National Capital Territory of Delhi Territory Capital National Malopolskie Zeeland

20 Slovensko Stredné 60 Anatoliki Makedonia - Makedonia Anatoliki Thraki Del-Alfold/Southern Great Great Plain Del-Alfold/Southern Vlaams Gewest Ticino Alsace Southern-Kanto Antofagasta Berlin Zahodna Slovenija Zahodna Ciudad Autónom DistrictColumbia of

15 Hovedstaden East of England of East 40 Sydsverige Saskatchewan Norte

10 Helsinki-Uusimaa Tacna Kaluga Oblast Kaluga Oslo og Akershus og Oslo Southern and Southern Eastern

Victoria 20 Atlántico Gauteng Capital Region Capital Auckland Region Auckland 5 São Paulo

0 0

1 2 http://dx.doi.org/10.1787/888933363059

1.22. Levels of air pollution for metropolitan 1.23. Regional variation in death rate due populations, 2014 to diseases of the respiratory system, 2010

<10 10 to 15 15 to 25 25 to 35 >35 Minimum Country average Maximum

SVK (1) PRT R. A. de KOR (10) Madeira HUN (1) ESP Galicia CZE (3) AUT (3) POL Warminsko-Mazurskie SVN (1) GRC Voreio Aigaio BEL (4) DEU (24) DEU Bremen POL (8) NLD (5) NLD Limburg CHE (3) ITA Liguria JPN (36) DNK (1) NOR Hedmark og Oppland FRA (15) GRC (2) CHE Ticino MEX (33) FRA Limousin ITA (11) OECD29 (281) HUN Northern Hungary GBR (15) USA (70) AUT Niederösterreich ESP (8) SWE Mellersta Norrland FIN (1) CAN (9) IRL Border, Midland and Western SWE (3) PRT (2) CZE Jihozápad CHL (3) SVK Bratislavský kraj NOR (1) IRL (1) SVN Vzhodna Slovenija EST (1) Pohjois- ja Itä-Suomi AUS (6) FIN 0 20406080100 0 50 100 150 200 Country (No. of cities) % Deaths per 100 000 people

1 2 http://dx.doi.org/10.1787/888933363062 1 2 http://dx.doi.org/10.1787/888933363075

OECD REGIONS AT A GLANCE 2016 © OECD 2016 39 1. WELL-BEING IN REGIONS Civic engagement and governance

Well-functioning democracies require people to engage high levels of perceived corruption (above 80%) with and participate in the different aspects and activities of average regional gaps. Finally, the largest regional public life. Through engagement and participation disparities in perceived corruption are found in Canada, individuals influence and determine the political choices Chile, Mexico, Turkey and the United States (above that impact everyone’s lives and well-being. Civic 30 percentage points) (Figure 1.26). engagement and participation are necessary conditions for effective governance, while, at the same time, good quality of governance, through different institutional settings, can Source enhance citizens’ participation. Gallup World Poll (2015), www.gallup.com/services/170945/ Across OECD regions, people living in regions with higher world-poll.aspx. voter turnout in national elections often have a lower OECD (2015), OECD Regional Statistics (database), http:// perception of government corruption (Figure 1.24). In more dx.doi.org/10.1787/region-data-en. participative democracies it might be more difficult for elected officials to commit acts of corruption; on the other hand, less corrupt and more efficient public institutions Reference years and territorial level might motivate people’s participation and trust in institutions’ capacity to generate positive change. Voter turnout: 2006-14; TL2 (Greece, the Netherlands and The largest regional disparities in electoral participation to New Zealand, NUTS 1). national elections are presented in the United States, Perception of corruption: 2006-14; TL2 (New Zealand, Canada, Spain, Mexico, Chile and Portugal (above 20 NUTS 1; Estonia TL3). percentage points) (Figure 1.25). Denmark, Finland, New Zealand, Norway, Sweden and Further information Switzerland present relatively low levels of perceived corruption (below 30%) and small regional variations; the OECD Regional Well-Being: www.oecdregionalwellbeing.org/. Czech Republic, Greece, Israel, Italy and Portugal show very Boarini, R. and M. Díaz Ramírez (2015), “Cast a Ballot or Protest in the Street - Did our Grandparents Do More of Both?: An Age-Period-Cohort Analysis in Political Participation”, OECD Statistics Working Papers, No. 2015/02, Definition OECD Publishing, Paris, http://dx.doi.org/10.1787/ 5js636gn50jb-en. Voter turnout refers to the extent of electoral participation in national elections. It is defined as the Brezzi, M. and M. Díaz Ramirez (2016), “Building subjective percentage of individuals who cast a ballot in a well-being indicators at the subnational level: A national election with respect to the population preliminary assessment in OECD regions”, OECD Regional registered to vote. Data on voter turnout are gathered Development Working Papers, No. 2016/03, OECD Publishing, by National Statistical Offices and National Electoral Paris, http://dx.doi.org/10.1787/5jm2hhcjftvh-en. Management Bodies. Perception of corruption, which is intended to capture Figure notes elements of the quality of the government, is calculated as the percentage of people that responded 1.24: Each dot represents a TL2 region in the OECD countries. Greece and “Yes” to the question “Is corruption widespread the Netherlands are not included. throughout the government in (this country), or not?”. 1.25: Latest available years: Canada, Denmark, Estonia, Poland, Portugal, Spain, Switzerland and United Kingdom 2015. Australia, Austria, The indicator on perception of corruption was Chile, Czech Republic, Germany, Iceland, Israel, Italy, Luxembourg calculated using microdata from the Gallup World and Norway 2013. Finland, France, Greece, Korea, Mexico and Poll (see Brezzi and Díaz Ramírez, 2016). United States 2012. Ireland, Netherlands and New Zealand 2011. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

40 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

Civic engagement and governance

1.24. Regional voter turnout in national elections and perception of corruption; average 2006-14 Perception of corruption % 100

10 35 45 55 65 75 85 95 Voter turnout %

1 2 http://dx.doi.org/10.1787/888933363089

1.25. Regional variation in national elections 1.26. Per cent of people who believe voter turnout, 2014 the government is corrupt, average 2006-14

Minimum Country average Maximum Minimum Country average Maximum (region)

USA West Virginia District of Columbia MEX Zacatecas ESP Melilla Castile-La Mancha CHL Maule MEX Michoacan Yucatan CAN Quebec ITA Calabria Bolzano-Bozen TUR Thrace PRT Azores Lisbon USA West Virginia FIN Åland Helsinki-Uusimaa AUS Queensland AUT Vorarlberg Burgenland ESP Basque Country POL Warmian-Masuria Mazovia ITA Abruzzo CAN Newfoundland and Labrador Prince Edward Island FRA Limousin CHL Tarapacá Maule DEU Saarland East Macedonia GRC Ionian Islands West Greece GRC Shikoku Northeast Estonia North Estonia JPN EST Lubusz TUR Northern Aegean POL Northeastern Anatolia - East PRT Alentejo GBR Northern Ireland Scotland Overijssel AUS Northern Territory Tasmania NLD Espace Mittelland CZE Northwest Prague CHE SVK West Slovakia DEU Saxony-Anhalt Baden-Württemberg GBR Wales SVK East Slovakia Bratislava Region Northwest Corsica CZE FRA Brittany ISR Tel Aviv District ISR Southern District Central District KOR Capital Region HUN Southern Great Plain Central Hungary SWE Upper Norrland NLD South Limburg Utrecht HUN Central Transdanubia CHE Lake Geneva Region Ticino AUT Carinthia NOR Northern Norway Oslo and Akershus DNK Zealand JPN Shikoku Hokkaido NOR South-Eastern Norway SVN Eastern Slovenia Western Slovenia BEL Wallonia KOR Jeju Gyeongbuk Region EST Central Estonia BEL Wallonia Flemish Region IRL Border, Midland, Western ISL Capital Region Other Regions ISL Capital Region SWE South Sweden Småland with Islands FIN Western Finland NZL North Island South Island SVN Eastern Slovenia DNK Northern Jutland Central Jutland NZL North Island IRL Southern and Eastern Border, Midland, Western LUX Luxembourg LUX Luxembourg 0 20 40 60 80 100 120 % 0 20 40 60 80 100% 120 1 2 http://dx.doi.org/10.1787/888933363098 1 2 http://dx.doi.org/10.1787/888933363109

OECD REGIONS AT A GLANCE 2016 © OECD 2016 41 1. WELL-BEING IN REGIONS Subjective well-being in regions

Subjective well-being reflects the notion of measuring how East (Turkey) to 8.6 in Campeche (Mexico). Mexico, Chile and people experience and evaluate their lives. It includes Turkey display the largest regional differences in life evaluation of life as a whole (generally referred as “life satisfaction, more than 2 points on a 0-10 point scale satisfaction”), evaluations of particular domains of life (for (Figure 1.27). example, “satisfaction with time available for leisure”), Good interpersonal relations, social network supports and feelings and emotions, as well as measures of general trust in others and institutions are considered “meaningfulness” or “purpose” in life. People’s evaluations important sources of individual well-being and social of different domains and their expectations are useful cohesion. Not only do they represent additional resources information to guide policy making. to the material and cultural ones, but they can also improve While in many OECD countries data on life satisfaction are performance of institutions and reduce transaction costs. now available from official sources, only in a few cases In most OECD regions, at least 80% of people report having these data are representative at the subnational level. The someone to rely on in case of need. The exceptions are data shown here are estimates derived by a unique data Korea where the values range between 73% to 79%, and source, the Gallup World Poll, reweighted to improve Mexico, Chile, Turkey and Greece where regional regional representativeness. These data represent an differences are very large with some regions below 75% innovation in the OECD Regional Well-Being Database that (Figure 1.28). previously included only well-being dimensions measured by objective indicators. The average values of life satisfaction by country vary from 4.9 Source in Hungary to 7.6 in Denmark and Switzerland. The regional values, instead, range from 4.4 in the Mediterranean Region Gallup World Poll (2015), www.gallup.com/services/170945/ world-poll.aspx. See Annex B for data sources and country-related metadata. Definition Life satisfaction is expressed as the mean score on an Reference years and territorial level 11-point scale (based on the Cantril ladder measure). Average 2006-14; TL2 (Estonia, TL3). It is measured using a survey question in which respondents are asked “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. Further information The top of the ladder represents the best possible life for you and the bottom of the ladder represents the OECD Regional Well-Being: www.oecdregionalwellbeing.org/. worst possible life for you. On which step of the Brezzi, M. and M. Díaz Ramírez (2016), “Building subjective ladder would you say you personally feel you stand at well-being indicators at the subnational level: A this time?”. preliminary assessment in OECD regions”, OECD Regional Perceived social network support is based on the Development Working Papers, No. 2016/03, OECD Publishing, survey question: “If you were in trouble, do you have Paris, http://dx.doi.org/10.1787/5jm2hhcjftvh-en. relatives or friends you can count on to help you OECD (2013), OECD Guidelines on Measuring Subjective Well- whenever you need them, or not?”. The data shown here reflect the percentage of the regional sample being, OECD Publishing, Paris, http://dx.doi.org/10.1787/ responding “Yes”. 9789264191655-en. The indicators on life satisfaction and social network in regions were calculated using microdata from the Figure note Gallup World Poll (see Brezzi and Díaz Ramírez, 2016). Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

42 OECD REGIONS AT A GLANCE 2016 © OECD 2016 1. WELL-BEING IN REGIONS

Subjective well-being in regions

1.27. Estimated regional variation in life satisfaction Mean satisfaction with life; 0-10 points scale; average 2006-14

Minimum Country value Maximum

1 2 http://dx.doi.org/10.1787/888933363115

1.28. Estimated regional variation in perceived social network support Percentage of people who report having relatives or friends they can count on, average 2006-14

Minimum Country value Maximum

% 100

1 2 http://dx.doi.org/10.1787/888933363124

OECD REGIONS AT A GLANCE 2016 © OECD 2016 43

2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Population and population changes in regions How metropolitan areas contribute to population change Regional contributions to GDP growth Regional economic disparities Contribution of metropolitan areas to national economies Regional contributions to change in employment Productivity growth in regions Where productivity gains are happening Regional specialisation and productivity growth Impact of the crisis on regional economic disparities Employment and unemployment in metropolitan areas Regional concentration of innovation related resources Regions and venture capital Regional differences in highly-skilled workers Regional patterns of co-patenting Patent activity in metropolitan areas

The data in this chapter refer to regions in OECD and non-OECD countries, and to metropolitan areas in OECD countries. Regions are classified on two territorial levels reflecting the administrative organisation of countries. Large (TL2) regions represent the first administrative tier of subnational government. Small (TL3) regions are contained in a TL2 region. Metropolitan areas are identified on the basis of population density and commuting journeys, independently of administrative boundaries.

OECD REGIONS AT A GLANCE 2016 © OECD 2016 45 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Population and population changes in regions

Climatic and environmental conditions together with In all countries, with the exception of Ireland, economic opportunities and availability of services explain predominantly rural remote regions displayed on average a the geographic distribution of population within countries. decrease in population for the years 2000-14. On the other In 2014, almost half of the population of the OECD (46%) hand, populations grew in predominantly rural regions lived in predominantly urban regions, which accounted for close to a city in the United States, Ireland, Switzerland and 6% of the total area. More than 70% of the population lived Mexico, while in the remaining countries, predominantly in predominantly urban regions in the United Kingdom, rural regions close to a city lost population (Figure 2.3). the Netherlands, and Australia (Figure 2.1). On average, OECD population grew at an annual rate of Predominantly rural regions accounted for more than one- 0.7% in the period 2000-14. The ten regions with the highest quarter of the population and more than 80% of land area. population growth rate are found in Mexico, Canada and In Ireland, the Slovak Republic, Hungary, Estonia, Austria, Spain, with an annual population growth rate above 3.7%. Slovenia and Greece, the share of the national population Switzerland, Australia, Chile and the United Kingdom in rural regions was more than twice as high as the OECD displayed positive population growth rates in almost all average (Figure 2.1). their regions (above 90%) between 2000 and 2014, while in Rural regions in North America, Europe and Japan have Poland, Germany, Estonia, Japan and Hungary, the been further classified as either close to a large urban population decreased in more than 60% of the regions centre or remote. Among the 26 OECD countries with rural during the same period (Figures 2.4-2.7). regions, only in Sweden, Portugal, Greece, Denmark, and Canada does more than half of the rural population live in Source remote rural regions (Figure 2.1). OECD (2015), OECD Regional Statistics (database), http:// In 22 out of the 30 OECD countries considered, the share of dx.doi.org/10.1787/region-data-en. population in predominantly urban regions has increased in the past 15 years, and significantly in Estonia, Canada, See Annex B for data, source and country-related metadata. Finland, Japan, Austria, Turkey and Sweden (more than 2 percentage points). In almost all countries, predominantly Reference years and territorial level rural regions have seen a decrease in population, with the 2000-14; TL3. exception of Ireland, the United States, Chile, Switzerland and Belgium (Figure 2.2). TL2 regions in Brazil, China, Colombia, India, Peru, Russian Federation and South Africa. The extended OECD typology is applied only to North America, Europe and Japan. Definition OECD has established a regional typology to take into Further information account geographical differences and enable Brezzi, M., L. Dijkstra and V. Ruiz (2011), “OECD Extended meaningful comparisons between regions belonging to Regional Typology: The Economic Performance of the same type. All regions in a country have been Remote Rural Regions”, OECD Regional Development classified as predominantly rural, intermediate and Working Papers, 2011/06, OECD Publishing. http:// predominantly urban. This typology has been refined dx.doi.org/10.1787/5kg6z83tw7f4-en. by introducing a criterion of distance (driving time) to Eurostat (2013), Urban-Rural typology, http://ec.europa.eu/ large urban centres. Thus a predominantly rural region eurostat/web/rural-development/methodology. is classified as predominantly rural remote (PRR) if at least 50% of the regional population needs more than one hour to reach a large urban centre; otherwise, the Figure notes

rural region is classified as predominantly rural close 2.1-2.3: Latest available year 2010 for Mexico. to a city (PRC). The extended typology has been applied 2.2-2.3: First available year 2001 for Australia, Greece, Japan, Korea, to North America, Europe and Japan (see Annex A for Turkey; 2003 for the Netherlands. Denmark is not included for lack of the detailed methodology). Denmark is not included for lack of regional data on comparable years. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

46 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Population and population changes in regions

2.1. Distribution of population and area by type of region, 2014

Predominantly urban Intermediate Predominantly rural close Predominantly rural remote Population Area GBR GBR NLD NLD AUS AUS KOR KOR BEL BEL ESP ESP JPN JPN CAN CAN PRT PRT CHL CHL MEX MEX GRC GRC NZL NZL EST EST USA USA DEU DEU CHE CHE ITA ITA AUT AUT FRA FRA TUR TUR FIN FIN POL POL IRL IRL CZE CZE NOR NOR SWE SWE DNK DNK HUN HUN SVK SVK ISL ISL SVN SVN OECD OECD LVA LVA LTU LTU

0 20406080100 0 20406080100 % % 1 2 http://dx.doi.org/10.1787/888933363136

2.2. Change in the share of population by type 2.3. Change in the share of population living of region, 2000-14 in rural regions, 2000-14

Urban Intermediate Rural Rural remote Rural close

ESTEST NORNOR NOR ISLISL CAN TURTUR CANCAN FIN FINFIN PRT AUTAUT SWE PRTPRT AUSAUS HUN SVNSVN EST SWESWE KORKOR AUT HUNHUN JPN JPNJPN ITAITA TUR CZECZE MEX DEUDEU ESPESP ESP FRAFRA SVN MEXMEX GRC GRCGRC POLPOL USA SVKSVK CHE GBRGBR NLDNLD ITA BELBEL FRA CHECHE USAUSA GBR CHLCHL POL IRLIRL OECD30OECD30 CZE LTULTU DEU LVALVA SVK - 6- 4- 2 0 2 4 6 IRL percentage point -3 -2 -1 0 1 2 3 percentage point

1 2 http://dx.doi.org/10.1787/888933363149 1 2 http://dx.doi.org/10.1787/888933363159

OECD REGIONS AT A GLANCE 2016 © OECD 2016 47 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Population and population changes in regions

2.4. Regional population growth: Asia and Oceania, 2000-14 Average annual growth rate, TL3 regions

Higher than 3% Between 2% and 3% Between 1% and 2% Between 0.5% and 1% Between 0% and 0.5% Lower than 0% Data not available

Okinawa (JPN)

This map is for illustrative purposes and is without prejudice to the status of or sovereignty over any territory covered by this map. Source of administrative boundaries: National Statistical Ofﬁces and FAO Global Administrative Unit Layers (GAUL).

1 2 http://dx.doi.org/10.1787/888933363169

48 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Population and population changes in regions

2.5. Regional population growth: Europe, 2000-14 Average annual growth rate, TL3 regions

Higher than 3% Between 2% and 3% Between 1% and 2% Between 0.5% and 1% Between 0% and 0.5% Lower than 0% Data not available

Madeira (PRT)

Canarias Acores This map is for illustrative purposes and is without prejudice to the status of or sover- (ESP) (PRT) eignty over any territory covered by this map. Source of administrative boundaries: National Statistical Ofﬁces and FAO Global Administrative Unit Layers (GAUL).

1 2 http://dx.doi.org/10.1787/888933364081

OECD REGIONS AT A GLANCE 2016 © OECD 2016 49 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Population and population changes in regions

2.6. Regional population growth: Americas, 2000-14 Average annual growth rate, TL3 regions

Higher than 3% Between 2% and 3% Between 1% and 2% Between 0.5% and 1% Between 0% and 0.5% Lower than 0% Data not available

CHILE

This map is for illustrative purposes and is without prejudice to the status of or sover- Hawaii eignty over any territory covered by this map. (USA) Source of administrative boundaries: National Statistical Ofﬁces and FAO Global Administrative Unit Layers (GAUL).

1 2 http://dx.doi.org/10.1787/888933364093

50 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Population and population changes in regions

2.7. Regional population growth: Emerging economies, 2000-14 Average annual growth rate, TL2 regions

Higher than 3% Between 2% and 3% Between 1% and 2% Between 0.5% and 1% Between 0% and 0.5% Lower than 0% Data not available

COLOMBIA

PERU

BRAZIL SOUTH AFRICA

1 2 http://dx.doi.org/10.1787/888933364107

OECD REGIONS AT A GLANCE 2016 © OECD 2016 51 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS How metropolitan areas contribute to population change

Today, half of the OECD population live in metropolitan population grew at a faster rate in the city centre than in the areas – cities with more than 500 000 people. commuting zone, particularly evident in Japanese cities. Administrative boundaries do not always match up with The number of local governments per 100 000 people – a where people live, work and spend leisure time. Therefore, measure of administrative fragmentation of the metropolitan metropolitan areas are here defined as functional urban area–variesfromaround24intheCzechRepublictoless areas (FUA) – densely populated cities and commuting than 0.5 in Mexico, the United Kingdom, Ireland and Korea zones with high levels of commuting towards the city. (Figure 2.9). On average, municipalities in OECD metropolitan There are 1 197 functional urban areas in the 30 OECD areas concentrate more than 300 000 people. countries considered, and 281 of them are classified as metropolitan areas, having a population larger than 500 000 people. Source In the last 15 years, the population in metropolitan areas has OECD (2015), “Metropolitan areas”, OECD Regional Statistics been growing at a faster rate in the commuting zones rather (database), http://dx.doi.org/10.1787/data-00531-en. than in the city centres. The sub-urbanisation is particularly strong in the commuting zones of large metropolitan areas (with more than 1.5 million people). In these areas Reference years and territorial level population grew at a rate of 1.6% while the city centre grew at a rate below 1% (Figure 2.8). In contrast, in small For lack of comparable data on commuting, the FUAs have metropolitan areas (with a population between 500 000 and not been identified in Iceland, Israel, New Zealand and 1.5 million) in Australia, Japan, and Korea the urban Turkey. The FUA of Luxembourg does not appear in the figures since it has a population below 500 000 inhabitants.

Further information Definition OECD (2012), Redefining “Urban”: A New Way to Measure 281 Metropolitan areas have been identified in Metropolitan Areas, OECD Publishing, Paris, http:// 30 OECD countries according to the OECD-EU dx.doi.org/10.1787/9789264174108-en. methodology that identifies metropolitan areas on the basis of densely populated cities and their commuting zones (travel to work journeys) to reflect Figure notes

the economic geography of the population’s daily 2.8-2.9: Metropolitan population figures are estimates based on commuting patterns (see Annex A for details). municipal figures for the last two census available for each country. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

52 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

How metropolitan areas contribute to population change

2.8. Yearly population growth of metropolitan areas in city and commuting area, 2000-14

Total City Commuting zone

Metropolitan areas: Pop. above 1.5 mln Small metro: Pop. between 500 000 and 1.5 mln Large metro: Pop.above 1.5 mln % 3

2.5

1.5

0.5

0 Metro Small Large Metro Small Large Metro Small Large Metro Small Large Europe (22) Americas (4) Asia and Oceania (3) OECD (29) Continents (No. of countries)

1 2 http://dx.doi.org/10.1787/888933363175

2.9. Administrative fragmentation of metropolitan areas, 2014

Country (No. of cities)

1 2 http://dx.doi.org/10.1787/888933363185

OECD REGIONS AT A GLANCE 2016 © OECD 2016 53 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Regional contributions to GDP growth

Some regions generate larger gross domestic product (GDP) Estonia and the Slovak Republic, above 15 percentage than others. In 2013, the OECD regions with largest GDP, points of the population share. Predominantly rural areas representing 20% of total population, generated 26% of generated 15% of total GDP, with more than 40% in Ireland OECD GDP. In Hungary, Poland, the Slovak Republic, and the and the Slovak Republic. United Kingdom, regions with the highest economic output During the period 2000-13, the average value of the yearly and totalling 20% of the population, contributed to at least GDP growth rate was 1.4% among OECD regions, 1.5% and one-third of the national GDP. 1.3% in predominantly urban and predominantly rural GDP growth was even more regionally concentrated than regions, respectively. Differences in regional GDP annual GDP production in many OECD countries. On average, 20% of growth rates between urban and rural regions were larger OECD population in the regions with the fastest GDP growth than 1 percentage point per year in Hungary, Estonia, contributed to 36% of the OECD GDP growth in 2000-13 Sweden, Denmark, Greece, Norway and France. On average, (Figure 2.10). At country level, the regional contribution to GDPgrowthrateswerelowerinruralregionsthanurban growth was very concentrated in Portugal, Italy, Denmark, regions in 18 out of 24 countries, while in Austria, Korea, Greece and Canada where the 20% of the most dynamic Switzerland, the Netherlands, Germany and Belgium regions in terms of GDP growth rates were responsible for predominantly rural regions on average performed better 50% or more of the national growth in 2000-13 (Figure 2.10). than predominantly urban ones (Figure 2.12). Predominantly urban regions attract the largest share of Wide differences in regional growth do not seem to be economic activity. In 2013, 55% of total GDP in OECD associated with faster national growth; Estonia and the countries was produced in urban regions (where 47% of Slovak Republic displayed a national growth rate higher OECD active population live), and reached 75% or more of than double the OECD average and limited regional national GDP in the United Kingdom, the Netherlands, and differences (Figures 2.13-2.16). Belgium (Figure 2.11). The GDP share of predominantly urban regions was particularly pronounced in Hungary, Source

OECD (2015), OECD Regional Statistics (database), http:// Definition dx.doi.org/10.1787/region-data-en. GDP is the standard measure of the value of the production activity (goods and services) of resident Reference years and territorial level producer units. Regional GDP is measured according to the definition of the System of National Accounts 2000-13; TL3. (SNA 2008). To make comparisons over time and across Australia, Brazil, Canada, Chile, China, India, Indonesia, countries, it is expressed at constant prices (year 2010), Mexico, Russian Federation, South Africa, Turkey and using the OECD deflator and then it is converted into United States, TL2 regions. USD purchasing power parities (PPPs) to express each Regional GDP is not available for Iceland and Israel. country’s GDP in a common currency. The top 20% fastest growing regions are defined as those with the highest GDP growth rate until the Figure notes equivalent of 20% of the national population is reached. OECD has established a regional typology to take into 2.10-2.12: Available years: Austria, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Poland, Spain, Sweden, Colombia, Russian account geographical differences and enable Federation and Latvia 2000-12; Japan 2001-12; Mexico 2003-13; meaningful comparisons between regions belonging to Portugal 2000-14; Turkey GVA data 2004-11; China and Indonesia the same type. All regions in a country have been 2004-12; India 2001-13; Lithuania 2005-12. Norway and Switzerland classified as predominantly rural, intermediate and are excluded from the figures due to lack of data over the period. predominantly urban. (see Annex A for the detailed 2.11: Only countries where GDP is available for TL3 regions. methodology). 2.10 and 2.12: Germany non-official grid regions. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

54 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional contributions to GDP growth

2.10. Contribution to national GDP growth by top 20% fastest growing TL3 regions, 2000-13 % 90

1 2 http://dx.doi.org/10.1787/888933363194

2.11. Distribution of GDP by type of TL3 regions, 2.12. GDP annual growth rate by type of TL3 regions, 2013 2000-13

Predominantly Intermediate Predominantly Urban Intermediate Rural urban rural KOR GBR SVK NLD POL BEL EST KOR CZE ESP IRL EST AUT JPN ESP PRT GRC FIN DEU GBR FRA SVN CHE BEL IRL NLD ITA NOR POL DEU AUT SWE HUN CHE FIN HUN CZE FRA DNK JPN NOR SWE DNK SVK ITA SVN PRT OECD24 GRC LVA NZL LTU LUX 0 20406080100 -1 1 3 5 % % 1 2 http://dx.doi.org/10.1787/888933363200 1 2 http://dx.doi.org/10.1787/888933363216

OECD REGIONS AT A GLANCE 2016 © OECD 2016 55 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional contributions to GDP growth

2.13. Regional GDP growth: Asia and Oceania, 2000-13 Average annual growth rate (constant 2010 USD PPP), TL3 regions

Higher than 4% Between 3% and 4% Between 2% and 3% Between 1% and 2% Between 0% and 1% Lower than 0% Data not available

Okinawa (JPN)

1 2 http://dx.doi.org/10.1787/888933364110

56 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional contributions to GDP growth

2.14. Regional GDP growth: Europe, 2000-13 Average annual growth rate (constant 2010 USD PPP), TL3 regions

Higher than 4% Between 3% and 4% Between 2% and 3% Between 1% and 2% Between 0% and 1% Lower than 0% Data not available

Madeira (PRT)

Acores This map is for illustrative purposes and is Canarias (PRT) without prejudice to the status of or sover- (ESP) eignty over any territory covered by this map. Source of administrative boundaries: National Statistical Ofﬁces and FAO Global Administrative Unit Layers (GAUL).

Germany non-official grid (NOG) regions; Turkey TL2. 1 2 http://dx.doi.org/10.1787/888933364128

OECD REGIONS AT A GLANCE 2016 © OECD 2016 57 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional contributions to GDP growth

2.15. Regional GDP growth: Americas, 2000-13 Average annual growth rate (constant 2010 USD PPP), TL2 regions

Higher than 4% Between 3% and 4% Between 2% and 3% Between 1% and 2% Between 0% and 1% Lower than 0% Data not available

CHILE

This map is for illustrative purposes and is Hawaii without prejudice to the status of or sover- (USA) eignty over any territory covered by this map. Source of administrative boundaries: National Statistical Ofﬁces and FAO Global Administrative Unit Layers (GAUL).

1 2 http://dx.doi.org/10.1787/888933364135

58 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional contributions to GDP growth

2.16. Regional GDP growth: Emerging economies, 2000-13 Average annual growth rate (constant 2010 USD PPP), TL2 regions

Higher than 4% Between 3% and 4% Between 2% and 3% Between 1% and 2% Between 0% and 1% Lower than 0% Data not available

COLOMBIA

PERU

BRAZIL SOUTH AFRICA

1 2 http://dx.doi.org/10.1787/888933364145

OECD REGIONS AT A GLANCE 2016 © OECD 2016 59 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Regional economic disparities

Regional differences in gross domestic product (GDP) per While the Gini index provides a measure of the overall capita within non-OECD countries are often substantial inter-regional disparities in a country, the poverty rates and larger than among OECD countries. According to the measure the share of people living in the bottom part of the Gini index, the emerging economies – Indonesia, Colombia income distribution and can provide an indication of the and the Russian Federation – displayed the greatest different economic implications of disparities within a disparity in GDP per capita in 2013, with Chile, Mexico, the country. Regional disparities as measured by the Gini index Slovak Republic and Ireland showing greatest disparity in GDP per capita are of the same magnitude in the United among the OECD countries (Figure 2.17). States and in the Czech Republic, for example, while the During 2000-13 regional disparities increased in 18 out of percentage of the national population in poverty in the the 32 countries considered. Significant increases can be former is more than three times higher than in the latter found in Ireland, Australia, the Slovak Republic and France (Figure 2.19). (Figure 2.17). Regional differences in GDP per capita, measured by the range between the region with the highest and the lowest Source GDP per capita, were markedly high in Mexico, Chile and the United States where some regions were at least three times OECD (2015), OECD Regional Statistics (database), http:// richer than the national average, and other regions had dx.doi.org/10.1787/region-data-en. values lower than half of the national average (Figure 2.18). OECD (2015), “Deflator and purchasing power parities”, OECD National Accounts (database), http://dx.doi.org/10.1787/ na-data-en. OECD (2015), “Income distribution”, OECD Social and Definition Welfare Statistics (database), http://dx.doi.org/10.1787/ GDP is the standard measure of the value of the socwel-data-en. production activity (goods and services) of resident producer units. Regional GDP is measured according to the definition of the System of National Accounts Reference years and territorial level (SNA 2008). To make comparisons over time and across countries, it is expressed at constant prices 2000-13; TL3. (year 2010), using the OECD deflator and then it is Australia, Canada, Chile, Mexico, Turkey and the United converted into USD purchasing power parities (PPPs) States TL2 regions. Germany non-official grid regions. to express each country’s GDP in a common currency. Brazil, China, Colombia, Indonesia, Russian Federation and GDP per capita is calculated by dividing the GDP of a South Africa TL2 regions. country or a region by its population. Regional GVA for Turkey. Regional GDP is not available for The Gini index is a measure of inequality among all Iceland and Israel. regions of a given country. The index takes on values between 0 and 1, with zero interpreted as no disparity. It assigns equal weight to each region regardless of its Figure notes size; therefore differences in the values of the index among countries may be partially due to differences 2.17: First available years: Japan and India 2001; Mexico 2003; in the average size of regions in each country. China 2004. 2.17-2.19: Last available year: Austria, Brazil, China, Colombia, Estonia, The poverty rate is the ratio of the number of people Finland, France, Germany, Hungary, Indonesia, Ireland, Italy, Japan, who fall below the poverty line and the total Latvia, Lithuania, Norway, Poland, Russian Federation, Spain, population; the poverty line is here taken as half the Sweden and Switzerland 2012. median household income. 2.19: Poverty rate, all countries 2012, Canada 2011. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

60 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional economic disparities

2.17. Gini index of inequality of GDP per capita 2.18. Regional variation in GDP per capita across TL3 regions, 2000 and 2013 (asa%ofnational average), 2013 (TL2)

2013 2000 Minimum Country average=100 Maximum

CHL MEX MEX CHL 618 SVK USA IRL SVK TUR CZE HUN TUR KOR CAN EST HUN POL BEL GBR GBR CAN DEU AUS ITA CHE FRA OECD31 NZL ITA POL DNK AUS AUT EST USA NLD NZL ESP FRA CZE GRC DEU SWE NOR PRT PRT AUT SVN DNK ESP NOR FIN IRL JPN FIN BEL KOR SWE CHE NLD SVN GRC JPN IDN RUS COL IDN RUS COL BRA IND CHN BRA LVA CHN LTU LVA ZAF LTU ZAF 0.0 0.1 0.2 0.3 0.4 0.5 % 0 100 200 300 400 500 % 1 2 http://dx.doi.org/10.1787/888933363221 1 2 http://dx.doi.org/10.1787/888933363233

2.19. Gini index of inequality of GDP per capita across TL3 regions and poverty rate after taxes and transfers (%), 2013 Poverty rate, after taxes and transfers (%) 20 USA TUR MEX 18

GRC gini Median 16 KOR ESP AUS PRT 14 ITA EST CAN 12 BEL NZL GBR POL HUN SVN AUT CHE 10 SWE IRL SVK NLD DEU NOR FRA 8 FIN CZE DNK 6

0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Inter-regional disparity (GINI index)

1 2 http://dx.doi.org/10.1787/888933363243

OECD REGIONS AT A GLANCE 2016 © OECD 2016 61 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional economic disparities

2.20. Regional GDP per capita: Asia and Oceania, 2013 (Constant 2010 USD PPP in thousands), TL3 regions

Higher than 35 Between 28 and 35 Between 20 and 28 Between 12 and 20 Between 6 and 12 Lower than 6 Data not available

Higher than 55 Between 40 and 55 Between 30 and 40 Between 20 and 30 Between 12 and 20 Lower than 12 Data not available

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62 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional economic disparities

2.21. Regional GDP per capita: Europe, 2013 (Constant 2010 USD PPP in thousands), TL3 regions

Higher than 35 Between 28 and 35 Between 20 and 28 Between 12 and 20 Between 6 and 12 Lower than 6 Data not available

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OECD REGIONS AT A GLANCE 2016 © OECD 2016 63 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional economic disparities

2.22. Regional GDP per capita: Americas, 2013 (Constant 2010 USD PPP in thousands), TL2 regions

Higher than 55 Between 40 and 55 Between 30 and 40 Between 20 and 30 Between 12 and 20 Lower than 12 Data not available

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64 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional economic disparities

2.23. Regional GDP per capita: Emerging economies, 2013 (Constant 2010 USD PPP in thousands), TL2 regions

Higher than 35 Between 28 and 35 Between 20 and 28 Between 12 and 20 Between 6 and 12 Lower than 6 Data not available

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OECD REGIONS AT A GLANCE 2016 © OECD 2016 65 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Contribution of metropolitan areas to national economies

In 2013, 281 metropolitan areas, where 49% of the OECD OECD area was around 30% in 2013. Such a gap is higher in population lived, generated 57% of gross domestic product the Americas and in Europe than in Asia and Oceania (GDP) and 51% of employment in the OECD area (Figure 2.26). Overall, GDP per worker is on average higher (Figure 2.24). in large metropolitan areas (with population above Nationally, the concentration of GDP ranges from 71% in 1.5 million) (Figure 2.26). Japan to less than 30% in Norway. Employment and population tend to be less concentrated than GDP, with the Source exception of Australia and Korea (Figure 2.24). The contribution of metropolitan areas to national GDP OECD (2015), “Metropolitan areas”, OECD Regional Statistics growth can be quite different across OECD countries. (database), http://dx.doi.org/10.1787/data-00531-en. Metropolitan areas in Norway, Japan and Denmark accounted for more than 75% of the national growth in the period 2000-13. In contrast, in Switzerland and the Reference years and territorial level Netherlands, metropolitan areas accounted for less than The OECD-EU definition of functional urban areas (FUA) 30% of the national growth (Figure 2.25). In general terms, has not been applied to Iceland, Israel, New Zealand and capital cities in countries with one metropolitan area such Turkey. The FUA of Luxembourg does not appear in the as in Norway, Denmark and Hungary, accounted for more figures since it has a population below 500 000 inhabitants. than 70% of the GDP national growth (Figure 2.25). While the overall economic performance of metropolitan areas was strong in the period 2000-13, some areas are Further information growing fast while others are stagnant or shrinking OECD (2012), Redefining “Urban”: A New Way to Measure (Figures 2.27 and 2.28). Indeed, while metropolitan areas Metropolitan Areas, OECD Publishing, Paris, http:// such as Centro (Mexico) and Perth (Australia) grew at an dx.doi.org/10.1787/9789264174108-en. annual average growth rate above 6% between the period 2000-12, Catania (Italy), Detroit (United States) and OECD (2015), The Metropolitan Century. Understanding Rotterdam (Netherlands) experienced the larger decreases Urbanisation and its Consequences, OECD Publishing, Paris, in terms of GDP over the same period (above -0.5%). http://dx.doi.org/10.1787/9789264228733-en. Metropolitan areas tend to be more productive than the rest of the economy. The productivity gap, measured as the Figure notes difference in terms of GDP per worker between the metropolitan areas and the rest of the economy, in the 2.24 and 2.26: Last available year: Austria, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Japan, Norway, Poland, Spain, Sweden and Switzerland 2012; Slovenia 2011. Metropolitan employment figures are estimates based on employment data at TL3 level except for Chile, Mexico, Poland and Portugal were TL2 are used and NOG for Canada. Australian and the United States figures are Definition provided by the Australian Bureau of Statistics and U.S. Bureau of Labour Statistics respectively. For all countries, metropolitan 281 Metropolitan areas have been identified in population is estimated on municipal population for the last two 30 OECD countries according to the OECD-EU available Population Census. methodology that identifies metropolitan areas on 2.25 and 2.27-2.28: Available years: Austria, Germany, Estonia, Spain, the basis of densely populated cities and their Finland, France, Hungary, Ireland, Italy, Poland, Sweden 2000-12; commuting zones (travel to work journeys) to reflect Switzerland and Norway 2008-12; Japan 2001-12, Mexico 2003-13 and the economic geography of the population’s daily the United States 2001-13. Italy, Greece and Portugal are excluded commuting patterns (see Annex A for details). from the figure due to lack of data on comparable years. 2.24-2.28: Metropolitan GDP figures are estimates based on GDP data at The metropolitan population in a country is given by TL3 level except for Australia, Canada, Chile and Mexico were TL2 are the national population residing in metropolitan used. United States figures are provided by the U.S. Bureau of areas. Economic Analysis. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

66 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Contribution of metropolitan areas to national economies

2.24. Per cent of population, GDP and employment in OECD metropolitan areas, 2013

Share of national GDP Share of national employment Share of national population

% 80 70 60 50 40 30 20 10 0

Country (No. of cities)

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2.25. Per cent of national GDP growth 2.26. Ratio between labour productivity contributed by the metropolitan in metropolitan areas and the rest areas 2000-13 of the economy, 2013

All metropolitan areas Largest contributor All metropolitan areas Metropolitan areas with pop. above 1.5 mln Metropolitan areas with pop. between 0.5-1.5 mln (No. of cities, Largest contributor) Country Continent (No. of countries) (1, Oslo) NOR (36, Tokyo) JPN (1, Copenhagen) DNK (15, Paris) FRA OECD (29) (1, Budapest) HUN (6, Perth) AUS (10, Seoul Incheon) KOR (1, Tallinn) EST (70, Houston) USA (9, Toronto) CAN Asia and Oceania (3) (266) OECD26 (3, Santiago) CHL (33, Mexico City) MEX (15, London) GBR (3, Stockholm) SWE (4, Brussels) BEL (1, Ljubljana) SVN Americas (4) (3, Prague) CZE (1, Dublin) IRL (3, Vienna) AUT (8, Warsaw) POL (24, Munich) DEU (8, Madrid) ESP (1, Helsinki) FIN Europe (22) (1, Bratislava) SVK (3, Zurich) CHE (5, Amsterdam) NLD 050100 -20 0 20 40 60 % %

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OECD REGIONS AT A GLANCE 2016 © OECD 2016 67 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Contribution of metropolitan areas to national economies

2.27. Metropolitan GDP growth: Asia, Europe and Oceania, 2000-13 Average annual growth rate (constant 2010 USD PPP), metropolitan areas

Higher than 3.5% Between 2.5% and 3.5% Between 1.5% and 2.5% Between 0% and 1.5% Lower than 0%

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Contribution of metropolitan areas to national economies

2.28. Metropolitan GDP growth: Americas, 2000-13 Average annual growth rate (constant 2010 USD PPP), metropolitan areas

Higher than 3.5% Between 2.5% and 3.5% Between 1.5% and 2.5% Between 0% and 1.5% Lower than 0%

1 2 http://dx.doi.org/10.1787/888933364207

OECD REGIONS AT A GLANCE 2016 © OECD 2016 69 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Regional contributions to change in employment

Between 2000 and 2014, differences in annual growth of employment rates across OECD countries were as large as Definition 2.3 percentage points, ranging from -0.6% in Portugal and Employed persons are all persons who during the Greece to 1.7% in Israel (Figure 2.29). referenceweekworkedatleastonehourforpayor Over the same period, differences in growth of regional profit, or were temporarily absent from such work. employment rates across regions were above 2 percentage Family workers are included. points in 13 out of 31 countries. The widest regional The employment rate is defined as the ratio between differences are found in Austria, Poland, Italy and the total employment (measured at the place of United States among the OECD countries, and Colombia residence) and population in the class age 15-64. and the Russian Federation among the emerging economies (Figure 2.29). The contribution to employment growth by the top 20% regions is defined as the regional share in In 12 out of 24 countries considered, employment creation employment creation of the regions with highest was higher in predominantly rural than in predominantly employment growth and corresponding to 20% of the urban regions in the period 2000-14 (Figure 2.30). national employment. Relatively few regions led national employment creation: on average, the regions accounting for 20% of OECD employment contributed to one-third of the overall employment growth in the OECD area between 2000 and Source 2014. The regional contribution to national employment creation was particularly pronounced in certain countries. OECD (2015), OECD Regional Statistics (database), http:// In Poland, the Czech Republic, Estonia, Hungary and Korea, dx.doi.org/10.1787/region-data-en. the regions accounting for 20% of employment, created 50% or more of national employment between 2000 and 2014 (Figure 2.31, panel A). Reference years and territorial level The economic crisis has slowed down the creation of 2000-14; TL3. employment also in the most dynamic regions in all OECD countries, with the exception of Turkey, Luxembourg, Israel and Mexico. On average in the period 2008-14, the annual Figure notes employment growth rate of the regions accounting for 20% of employment was negative in eight OECD countries and 2.29-2.31: TL2 regions for Belgium, Chile, Colombia, Greece, Iceland, lower than in the period 2000-07 in 22 countries; in Spain, Israel, Mexico, Netherlands, Poland, Portugal, Peru, Russian Federation and Slovenia. Canada and Germany Non Official for example, the regions accounting for 20% of national Grids. First available year: Australia, Canada, Colombia, Germany, employmenthadanannualemploymentgrowthrateof Japan, Peru and Slovenia 2001. Last available year: Austria, 0.9% in the period 2000-07 and -0.3% in 2008-14. In Korea, Czech Republic, Luxembourg and Switzerland 2013; Colombia 2012. the annual employment growth rate in the period 2008-14 Denmark and Turkey are excluded for lack of data on comparable of the top 20% regions was less than half of that in 2000-07 years. (Figure 2.31, panel B). Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

70 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional contributions to change in employment

2.29. Regional variation in employment annual 2.30. Employment average annual growth, growth rate, 2000-14 by type of region, 2000-14

Minimum Country average Maximum Urban Intermediate Rural

AUT EST POL AUT ITA USA DEU GBR HUN IRL FRA SVK AUS MEX SWE CHE KOR CHL DEU AUS CAN FIN ESP GRC JPN HUN POL JPN NLD FRA SWE GBR PRT FIN BEL CZE NLD SVK EST CZE NOR NOR ISR KOR ITA NZL CHE BEL ISL ESP SVN LUX PRT IRL COL RUS USA PER GRC

- 4 - 2 0 2 4 -2 -1 0 1 2 % % 1 2 http://dx.doi.org/10.1787/888933363284 1 2 http://dx.doi.org/10.1787/888933363297

2.31. Contribution to national employment growth and annual employment growth rates by top 20% regions

2000-14 2008-14 2000-07 % Contribution of top 20% to national employment growth 80 70 60 50 40 30 20 10 0

% Employment average annual growth rate of top 20% 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 -0.6

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OECD REGIONS AT A GLANCE 2016 © OECD 2016 71 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Productivity growth in regions

Labour productivity growth is considered a key indicator to Both poor performances in labour productivity and in labour assess regional competitiveness and an essential driver of utilisation are causes of the regional decline in GDP change in living standards. Regional living conditions are per capita (Figure 2.33). The 40 regions with the highest raised by continued gains in labour productivity, along with decline in GDP per capita rate during 2000-13 were an increase in labour utilisation. In fact, only economies essentially concentrated in 3 countries: Greece, Spain and that manage to simultaneously sustain employment and Italy (Figure 2.33). In the Spanish regions (Melilla, Balearic productivity growth will increase their gross domestic and Canary Islands) and some of the Greek regions (Central product (GDP) per capita and maintain it in the long run. Macedonia and Crete), the growth in labour productivity was Growth in regional GDP per capita is broken down into the offset by the sharp decline in labour utilisation. On the other contribution of labour productivity growth (here measured hand, the 18 Italian regions have seen a decrease in their as GDP per worker) and changes in labour utilisation productivity while labour utilisation stagnated (Figure 2.33). (measured as the ratio between employment at the place of Differences in labour productivity growth among regions work and population). are invariably the result of multiple national and local Among the 40 OECD regions with the highest GDP factors, including labour market policies and institutions per capita growth rate during 2000-13, labour productivity as well as innovation and the adoption of new growth is a major determinant compared to changes in technologies. As such, differences in labour productivity labour utilisation (Figure 2.32). In 32 of the 40 regions, growth among OECD regions are larger than among OECD labour productivity growth accounted for 75% or more of countries (Figures 2.34 and 2.35). the rise in GDP per capita. Source

OECD (2015), OECD Regional Statistics (database), http:// Definition dx.doi.org/10.1787/region-data-en. GDP is the standard measure of the value of the See Annex B for data sources and country-related metadata. production activity (goods and services) of resident producer units. Regional GDP is measured according Reference years and territorial level to the definition of the System of National Accounts (SNA 2008). To make comparisons over time and 2000-13; TL2. across countries, it is expressed at constant prices Denmark, Finland, Mexico, Spain, Switzerland and Turkey (year 2010), using the OECD deflator and then it is are not included for lack of regional data on comparable converted into USD purchasing power parities (PPPs) years. to express each country’s GDP in a common currency. Regional GDP is not available for Iceland and Israel. Regional labour productivity is measured as the ratio of constant GDP in 2010 prices, to total employment, where the latter is measured at place of work. This Further information means that productivity and GDP per capita trends may diverge in regions if there is commuting on a OECD (2013), Economic Policy Reforms 2013: Going for Growth, substantial scale. OECD Publishing, Paris, http://dx.doi.org/10.1787/growth- Labour utilisation is here measured as the ratio 2013-en. between the total employment at place of work and OECD (2015), Productivity Statistics, www.oecd.org/std/ regional population. productivity-stats/. In the decomposition of change in regional GDP per capita, changes in labour utilisation may partially Figure notes depend on labour mobility if there is commuting on a substantial scale in the region. 2.32-2.34: First available year: Korea 2004. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

72 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Productivity growth in regions

2.32. Contribution of labour productivity and labour utilisation to GDP per capita: Top 40 TL2 regions, ranked by GDP per capita growth rate, 2000-13 Growth in GDP per capita Growth in labour productivity Growth in labour utilisation North Dakota (USA) North Dakota (USA) North Dakota (USA) Newfoundland and Labrador (CAN) Newfoundland and… Newfoundland and Labrador… Bratislava Region (SVK) Bratislava Region (SVK) Bratislava Region (SVK) Southern Estonia (EST) Southern Estonia (EST) Southern Estonia (EST) Northeast Estonia (EST) Northeast Estonia (EST) Northeast Estonia (EST) Lower Silesia (POL) Lower Silesia (POL) Lower Silesia (POL) Chungcheong Region (KOR) Chungcheong Region (KOR) Chungcheong Region (KOR) Western Australia (AUS) Western Australia (AUS) Western Australia (AUS) West Slovakia (SVK) West Slovakia (SVK) West Slovakia (SVK) Lodzkie (POL) Lodzkie (POL) Lodzkie (POL) North Estonia (EST) North Estonia (EST) North Estonia (EST) Saskatchewan (CAN) Saskatchewan (CAN) Saskatchewan (CAN) Mazovia (POL) Mazovia (POL) Mazovia (POL) Central Slovakia (SVK) Central Slovakia (SVK) Central Slovakia (SVK) Central Estonia (EST) Central Estonia (EST) Central Estonia (EST) Greater Poland (POL) Greater Poland (POL) Greater Poland (POL) Podlasie (POL) Podlasie (POL) Podlasie (POL) Lublin Province (POL) Lublin Province (POL) Lublin Province (POL) Wyoming (USA) Wyoming (USA) Wyoming (USA) Lesser Poland (POL) Lesser Poland (POL) Lesser Poland (POL) Opole region (POL) Opole region (POL) Opole region (POL) Podkarpacia (POL) Podkarpacia (POL) Podkarpacia (POL) Jeolla Region (KOR) Jeolla Region (KOR) Jeolla Region (KOR) Silesia (POL) Silesia (POL) Silesia (POL) Pomerania (POL) Pomerania (POL) Pomerania (POL) East Slovakia (SVK) East Slovakia (SVK) East Slovakia (SVK) Gyeongnam Region (KOR) Gyeongnam Region (KOR) Gyeongnam Region (KOR) Jeju (KOR) Jeju (KOR) Jeju (KOR) West Estonia (EST) West Estonia (EST) West Estonia (EST) Lubusz (POL) Lubusz (POL) Lubusz (POL) Swietokrzyskie (POL) Swietokrzyskie (POL) Swietokrzyskie (POL) Gangwon Region (KOR) Gangwon Region (KOR) Gangwon Region (KOR) Warmian-Masuria (POL) Warmian-Masuria (POL) Warmian-Masuria (POL) Moravia-Silesia (CZE) Moravia-Silesia (CZE) Moravia-Silesia (CZE) Kuyavian-Pomerania (POL) Kuyavian-Pomerania (POL) Kuyavian-Pomerania (POL) Gyeongbuk Region (KOR) Gyeongbuk Region (KOR) Gyeongbuk Region (KOR) Southeast (CZE) Southeast (CZE) Southeast (CZE) Prague (CZE) Prague (CZE) Prague (CZE) Capital Region (KOR) Capital Region (KOR) Capital Region (KOR) Northern Territory (AUS) Northern Territory (AUS) Northern Territory (AUS) 02460246-2 0 2 4 % % % 1 2 http://dx.doi.org/10.1787/888933363314

2.33. Contribution of labour productivity and labour utilisation to GDP per capita: Bottom 40 regions, ranked by GDP per capita growth rate, 2000-13 Growth in GDP per capita Growth in labour productivity Growth in labour utilisation Central Greece (GRC) Central Greece (GRC) Central Greece (GRC) South Aegean (GRC) South Aegean (GRC) South Aegean (GRC) Balearic Islands (ESP) Balearic Islands (ESP) Balearic Islands (ESP) Melilla (ESP) Melilla (ESP) Melilla (ESP) Southeast South Holland (NLD) Southeast South Holland (NLD) Southeast South Holland (NLD) Umbria (ITA) Umbria (ITA) Umbria (ITA) Molise (ITA) Molise (ITA) Molise (ITA) Apulia (ITA) Apulia (ITA) Apulia (ITA) Veneto (ITA) Veneto (ITA) Veneto (ITA) Piedmont (ITA) Piedmont (ITA) Piedmont (ITA) Emilia-Romagna (ITA) Emilia-Romagna (ITA) Emilia-Romagna (ITA) Marche (ITA) Marche (ITA) Marche (ITA) Sicily (ITA) Sicily (ITA) Sicily (ITA) Province of Trento (ITA) Province of Trento (ITA) Province of Trento (ITA) Canary Islands (ESP) Canary Islands (ESP) Canary Islands (ESP) Ionian Islands (GRC) Ionian Islands (GRC) Ionian Islands (GRC) Friuli-Venezia Giulia (ITA) Friuli-Venezia Giulia (ITA) Friuli-Venezia Giulia (ITA) Central Macedonia (GRC) Central Macedonia (GRC) Central Macedonia (GRC) Abruzzo (ITA) Abruzzo (ITA) Abruzzo (ITA) Crete (GRC) Crete (GRC) Crete (GRC) Calabria (ITA) Calabria (ITA) Calabria (ITA) Lazio (ITA) Lazio (ITA) Lazio (ITA) Franche-Comté (FRA) Franche-Comté (FRA) Franche-Comté (FRA) East Macedonia - Thrace (GRC) East Macedonia - Thrace (GRC) East Macedonia - Thrace (GRC) Nevada (USA) Nevada (USA) Nevada (USA) Sardinia (ITA) Sardinia (ITA) Sardinia (ITA) Basilicata (ITA) Basilicata (ITA) Basilicata (ITA) Epirus (GRC) Epirus (GRC) Epirus (GRC) Georgia (USA) Georgia (USA) Georgia (USA) Ceuta (ESP) Ceuta (ESP) Ceuta (ESP) Brussels Capital Region (BEL) Brussels Capital Region (BEL) Brussels Capital Region (BEL) Lombardy (ITA) Lombardy (ITA) Lombardy (ITA) Delaware (USA) Delaware (USA) Delaware (USA) Campania (ITA) Campania (ITA) Campania (ITA) Michigan (USA) Michigan (USA) Michigan (USA) Tuscany (ITA) Tuscany (ITA) Tuscany (ITA) Valencia (ESP) Valencia (ESP) Valencia (ESP) Peloponnese (GRC) Peloponnese (GRC) Peloponnese (GRC) Thessaly (GRC) Thessaly (GRC) Thessaly (GRC) West Macedonia (GRC) West Macedonia (GRC) West Macedonia (GRC) -3 -2 -1 0 -2 -1 0 1 -3 -2 -1 0 1 % % % 1 2 http://dx.doi.org/10.1787/888933363323

OECD REGIONS AT A GLANCE 2016 © OECD 2016 73 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Productivity growth in regions

2.34. Annual growth of regional productivity: Asia, Europe and Oceania, 2000-13 Average annual growth in regional GDP per worker (constant 2010 USD PPP), TL2 regions

Higher than 4% Between 3% and 4% Between 2% and 3% Between 1% and 2% Between 0% and 1% Lower than 0% Data not available

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74 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Productivity growth in regions

2.35. Annual growth of regional productivity: Americas, 2000-13 Average annual growth in regional GDP per worker (constant 2010 USD PPP), TL2 regions

Higher than 4% Between 3% and 4% Between 2% and 3% Between 1% and 2% Between 0% and 1% Lower than 0% Data not available

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OECD REGIONS AT A GLANCE 2016 © OECD 2016 75 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Where productivity gains are happening

Regions with the highest productivity in the country (known Frontier and lagging regions have a clear differentiation in as ‘frontier regions’) play a central role in productivity terms of typology: regions that are mostly urban dominate growth and in the process of diffusing productivity gains the composition of the frontier with a share of 70%, within the country. A better understanding of the profiles of whereas 60% of mostly rural regions are among the lagging lagging regions and of how they can catch up is key to regions, due in particular to the low dynamism of remote achieving inclusive growth. Between 1995 and 2013, the rural regions (Figure 2.37). The gap in productivity can be labour productivity in frontier regions increased by a yearly mainly explained by economies of agglomeration which average of 1.6% compared to 1.3% for the lagging regions, benefit large cities. At the same time, some mostly urban widening the regional productivity gap by around 50%, from regions containing large cities are lagging in productivity, USD 21 000 to USD 31 000. This suggests that lagging regions like Florida (United States) and Gyeongbuk region (Korea). benefit only partially from the growth of frontier regions. When labour productivity growth is split into an effect After the economic crisis of 2008, the gap stabilised, mainly related to the gains in the frontier regions and an effect due to the slowdown of productivity growth in the frontier specific to the productivity gains of the region towards its regions (Figure 2.36). national frontier (catching-up effect), regions show a high connection between these two effects. In general, among the 20 regions with highest productivity growth, a dynamic frontier effect in the country fosters the regional catching- up (Figure 2.38). Exceptions are found in North Dakota Definition and Wyoming (United States) or Groningen (Netherlands) where the productivity gains are mostly due to the Labour productivity is measured by GDP per employee, catching-up effect. with the employment defined by the place of work. Regional GDP is measured according to the definition On the other hand, among the regions with negative of the System of National Accounts (SNA 2008). To productivity, which are not catching-up with the rest of the make comparisons over time and across countries, it is country, the country-specific frontier shift effect is negative expressed at constant prices (year 2010), using the or weak; thus these regions have not benefitted from OECD deflator and then it is converted into USD productivity gains at the frontier (Figure 2.39). purchasing power parities (PPPs) to express each country’s GDP in a common currency. Source Frontier and lagging regions are the top and bottom 10% of regions in GDP per employee which are defined OECD (2015), OECD Regional Statistics (database), http:// as those with the highest/lowest GDP per employee dx.doi.org/10.1787/region-data-en. until the equivalent of 10% of national employment is reached. Reference years and territorial level A TL2 region is considered mostly rural if less than half of its population lives in a functional urban area 2.36: 1995-2013; TL2. and mostly urban if the more than 70% of its 2.37-2.39: 2000-13; TL2. population lives in a functional urban area. Regional GDP is not available for Iceland, Israel and Turkey. The Malmquist index allows the decomposition of the productivity growth of a region between two effects, the frontier shift effect which is the change of regional Figure notes productivity related to the gain of productivity of 2.36-2.37: Averages of frontier and lagging regions of 19 OECD countries the frontier, and the catch-up effect which is the for which regional data are available over the period. For EU acceleration of the productivity of the region towards countries, labour productivity data as from 1995 has been estimated the frontier (see Annex C for details). by linking SNA1993 and SNA2008 data. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

76 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Where productivity gains are happening

2.36. Productivity growth gap between frontier 2.37. Share of regions belonging to frontier and lagging regions, 1995-2013 (TL2) and lagging regions by typology, 2013 (TL2)

Frontier regions Lagging regions Frontier TL2 regions 75% of regions Frontier regions containing very large cities (>1.5M pop.) Lagging TL2 regions USD per employee % 100 000 100

1.6% per year 90 000 80

81% of mostly urban frontier 80 000 60 regions contain very large cities

1.3% per 70 000 year 40 1.3% per year 60 000 20

50 000 0 Mostly urban Intermediate Mostly rural

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2.38. Decomposition of productivity growth 2.39. Decomposition of productivity growth between frontier shift and catch-up effect, between frontier shift and catch-up effect, top 20 regions, 2000-13 bottom 20 regions, 2000-13

Frontier shift effect Catch-up effect Frontier shift effect Catch-up effect

Kuyavian-Pomerania (POL) Abruzzo (ITA) Greater Poland (POL) Sardinia (ITA) North Dakota (USA) Lombardy (ITA) Lesser Poland (POL) Apulia (ITA) Bratislava Region (SVK) Sicily (ITA) Newfoundland & Labrador (CAN) Ionian Islands (GRC) Western Australia (AUS) Tuscany (ITA) East Slovakia (SVK) Marche (ITA) Estonia (EST) South Holland (NLD) West Pomerania (POL) Gisborne Region (NZL) Lower Silesia (POL) Friuli-Venezia Giulia (ITA) Saskatchewan (CAN) Calabria (ITA) Chungcheong Region (KOR) Veneto (ITA) Central Slovakia (SVK) Molise (ITA) Lublin Province (POL) Province of Trento (ITA) Groningen (NLD) Piedmont (ITA) Lubusz (POL) Umbria (ITA) Wyoming (USA) South Aegean (GRC) West Slovakia (SVK) Lazio (ITA) Podlasie (POL) Central Greece (GRC) -20246 -2.5 -1.5 -0.5 0.5 1.5 % % 1 2 http://dx.doi.org/10.1787/888933363353 1 2 http://dx.doi.org/10.1787/888933363365

OECD REGIONS AT A GLANCE 2016 © OECD 2016 77 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Regional specialisation and productivity growth

While deeply rooted in local history, geography, institutions and social capital, the production structure of regions keeps Definition evolving over time as a result of both macroeconomic Industries are defined according to the International changes and economic policies at the national or subnational Standard Industrial Classification (ISIC) Rev.4. Industry level. size is defined by the share of employment in that The primary sector (agriculture, fishing and forestry) is still an industry. Regional data on gross value added (GVA) and important employer in many regions in Turkey, Mexico, employment are available aggregated in ten sectors. Poland, Greece and Portugal. All these countries display a Regional productivity by sector is defined as the GVA large inter-regional variation in agricultural employment, in the sector divided by the number of employees in with a few regions still highly specialised in primary activities. the sector. It is expressed in average yearly growth One such highly specialised region is Northeastern Anatolia rates over available years. in Turkey where 60% of the labour force is employed in the primary sector (Figure 2.40). Most countries have Advanced/lagging regions are defined as those with large differences in the shares of employment in mining, GDP per capita in 2000 above/below national average manufacturing and utilities (electricity, gas and water). GDP per capita. Seven countries in Eastern Europe – the Czech Republic, the Slovak Republic, Hungary, Slovenia, Estonia, Portugal and Poland – had markedly higher shares of employment in this sector in 2013. The region of Central Transdanubia in Hungary Source has a high specialisation in this industry with more than 35% of the employment, as well as the Thrace in Turkey and OECD (2015), OECD Regional Statistics (database), http:// Central Moravia in the Czech Republic (Figure 2.40). The dx.doi.org/10.1787/region-data-en. sector of construction shows regional “outliers” where the See Annex B for data sources and country-related metadata. share of service jobs is much above the national average, like Corsica in France and Aosta Valley in Italy. Reference years and territorial level Differences in productivity changes have also been marked within countries, contributing largely to regional convergence 2000-13; TL2 OECD countries. or divergence. In the period 2000-13, productivity gains Branch accounts are not available for Iceland and Israel. in agriculture and manufacturing were similar between lagging regions (those with GDP per capita below the national Figure notes average in 2000) and advanced regions (GDP per capita above national average in 2000) (Figure 2.41). Lagging regions in 2.40-2.41: Last available year: Australia, Austria, Belgium, Canada, Canada, Finland, Japan and the Slovak Republic performed Czech Republic, Denmark, Finland, France, Greece, Hungary, Ireland, Japan, Korea, New Zealand, Poland, Slovak Republic, Slovenia, Spain, significantly better than advanced regions in agriculture; Sweden, United States 2012; New Zealand and construction in while in Ireland and the United States the same was evident Portugal 2011; Turkey, 2014. in manufacturing. The lower dynamism is apparent in the 2.41: First available year: Belgium and Japan 2009, Korea 2004, Canada construction sector, where labour productivity decreased in 2002. Germany, Mexico, Netherlands and Norway data are not 12 out of 21 countries, both in advanced and lagging regions. included for lack of regional data on comparable years. Turkey is Only in Japan was the productivity growth in lagging regions excluded for lack of data on GVA by industry. significantly higher than in advanced regions (Figure 2.41). Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

78 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional specialisation and productivity growth

2.40. Regional range of employment share (as a % of regional total employment) in selected industries, 2013 (TL2)

Minimum Country average Maximum

Agriculture, forestry, hunting and Mining, manufacturing and utilities Construction fishing 59.5 TUR TUR TUR MEX CZE CHL GRC MEX FRA POL HUN ISR CHL ESP GRC NZL CHL MEX PRT PRT DEU KOR KOR USA HUN ITA ITA ITA DEU AUS EST JPN PRT ESP SVK SVK ISL AUT CAN AUT POL JPN SVN FRA AUT FIN SWE BEL CAN GRC DNK FRA USA NZL AUS EST ESP NOR NOR POL CZE FIN NOR DNK AUS CHE SWE NZL KOR IRL ISR EST ISR BEL NLD NLD SVN SWE DEU ISL CZE SVK CHE ISL USA NLD IRL CHE CAN FIN BEL DNK SVN JPN IRL HUN 0 10 20 30 40 0 10 20 30 40 0 5 10 15

% % % 1 2 http://dx.doi.org/10.1787/888933363379

2.41. Annual rate of productivity growth in selected industries in 2000-13, by regional economic performance in 2000, TL2

Advanced regions Lagging regions Agriculture, forestry, hunting and Mining, manufacturing and utilities Construction fishing

CHE SVK SVK SVK JPN AUS BEL KOR JPN HUN POL CZE POL SWE BEL SWE HUN ESP AUS SVN NZL KOR CZE CAN DNK PRT KOR ESP ESP IRL FRA IRL DNK CAN FRA FRA CZE AUT USA SVN SVN AUT FIN PRT NZL HUN NZL DNK GRC USA FIN PRT JPN AUS POL GRC CAN SWE FIN BEL AUT IRL GRC USA -10 -5 0 5 10 15 -5 0 5 10 -5 0 5 10 % % % 1 2 http://dx.doi.org/10.1787/888933363381

OECD REGIONS AT A GLANCE 2016 © OECD 2016 79 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Impact of the crisis on regional economic disparities

The evolution of the economic disparity within a country is the crisis where it was decreasing before, whereas for other here measured by the growth of the difference in gross countries like Mexico, Canada and Slovenia, the effect was domestic product (GDP) per capita between the richest 20% the opposite (reduction after a growth) (Figure 2.43). of regions and the poorest 20% of regions. This gap has The interpretation of the evolution of the gap after 2008 grown in the OECD area at an average rate of 0.3% yearly in depends on the evolution of the situation of poorest regions the period 2000-13 (Figure 2.42). compared to richest. Japan, Germany, Canada, New Zealand Regional economic disparities, however, can be very and Chile had a reduction of the regional gap between 2008 different between countries and for the periods before and and 2013 due to a better performance of the poorest regions, after the economic crisis of 2008. Among the 27 OECD while in Finland, Portugal and Belgium, the reduction of the countries observed, 15 countries have grown in the inter- gap is due to a lower GDP per capita regression of poorest regional gap between richest and poorest regions for the regions than richest (Figure 2.43). period 2000-07, with Canada, Hungary, Mexico, the Slovak The gap increased in Ireland, Spain and for six other Republic and Australia having an increase of more than countries due to higher recession of poorest regions 1.5% per year. For the period 2008-13, this increase compared to richest, whereas it increased in Australia, concerns 14 countries, with the biggest increases in Poland, Slovak Republic and Korea due to faster growth in inequalities located in Ireland and Australia. The crisis had the richest regions (Figure 2.43). a sharp effect especially in Ireland, Spain and Sweden, countries for which the inter-regional gap increased after Source

OECD (2015), OECD Regional Statistics (database), http:// Definition dx.doi.org/10.1787/region-data-en. OECD (2015), “Deflator and purchasing power parities”, GDP is the standard measure of the value of the OECD National Accounts (database), http://dx.doi.org/10.1787/ production activity (goods and services) of resident na-data-en. producer units. Regional GDP is measured according to the definition of the System of National Accounts (SNA 2008). To make comparisons over time and Reference years and territorial level across countries, it is expressed at constant prices (year 2010), using the OECD deflator and then it is 2.42-2.43: GDP 2000-13; TL3. converted into USD purchasing power parities (PPPs) Australia, Canada, Chile, Mexico and United States only to express each country’s GDP in a common currency. TL2. Germany non-official grids. GDP per capita is calculated by dividing the GDP of a Regional GDP is not available for Iceland, Israel and Turkey. country or a region by its population. The top and bottom 20% regions are defined as those Figure notes with the highest/lowest GDP per capita until the equivalent of 20% of the national population is 2.42: Denmark, Norway and Switzerland are excluded due to lack of data reached. for 2000-13. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

80 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Impact of the crisis on regional economic disparities

2.42. Average annual growth of the GDP per capita gap between the richest 20% and the poorest 20% of TL3 regions, 2000-07, 2008-13 and 2000-13

2000-07 2008-13 2000-13 % 3 3 6% 2.5 2 2 1 1.5 0 1 -1 0.5 -2 0 -3 -0.5 -4 -1 -1.5 -5 -2 -6

1 2 http://dx.doi.org/10.1787/888933363398

2.43. Evolution of regional disparity in GDP per capita between the richest and poorest TL3 regions, Gap difference between 2008 and 2013 in percentage

Inter-regional gap decrease Inter-regional gap increase Poorest Poorest Poorest Poorest Poorest Poorest increased increased decreased increased decreased decreased more than and richest less than less than more than and richest Gap decrease Gap increase richest decreased richest richest richest increased FIN PRT MEX BEL AUT JPN EST DEU CAN SVN CZE NZL CHL CHE DNK OECD HUN GRC FRA NLD ITA KOR SVK NOR USA GBR SWE POL ESP AUS IRL CHN RUS ZAF LVA BRA LTU IDN IND COL -20 -10 0 10 20 30 % 1 2 http://dx.doi.org/10.1787/888933363403

OECD REGIONS AT A GLANCE 2016 © OECD 2016 81 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Employment and unemployment in metropolitan areas

Metropolitan areas have contributed to 60% of employment areas in Germany, Hungary and Estonia saw an increase of creation across OECD countries in the past 15 years. the average employment rate by at least 4 percentage However, the contribution to job creation varies points between 2007 and 2014 (Figure 2.44). substantially within and across countries. Metropolitan Unemployment has generally increased in OECD areas in Italy and Korea accounted for more than 80% of job metropolitan areas since the crisis, from 5.5% in 2008 to creation between 2000 and 2014, compared to less than 30% 6.6% in 2014. However unemployment in metropolitan in Switzerland and the Slovak Republic. areas evolved differently from country to country over this Since the economic crisis of 2008, jobs have recovered in period. While unemployment rates have increased on many metropolitan areas; however, 2014 employment rates average by more than 10 percentage points in the in metropolitan areas are still below 2007 levels in 19 out of metropolitan areas in Greece and Spain, metropolitan 28 OECD countries. The largest differences are observed in areas in Japan, Chile and Germany experienced a reduction Greece, Spain and Ireland where aggregated metropolitan in unemployment rates (Figure 2.45). employment rates in 2014 were on average 4 percentage points below the values of 2007. In contrast, metropolitan Source

OECD (2015), “Metropolitan areas”, OECD Regional Statistics (database), http://dx.doi.org/10.1787/data-00531-en. Definition

281 Metropolitan areas have been identified in Reference years and territorial level 30 OECD countries, according to the OECD-EU methodology that identifies metropolitan areas on The functional urban areas (FUA) have not been identified the basis of densely populated cities and their in Iceland, Israel, New Zealand and Turkey. The FUA of commuting zones (travel to work journeys) to reflect Luxembourg does not appear in the figures since it has a the economic geography of the population’s daily population below 500 000 inhabitants. commuting patterns (see Annex A for details). Employed persons are all persons who during the Further information reference week worked at least one hour for pay or profit, or were temporarily absent from such work. OECD (2012), Redefining “Urban”: A New Way to Measure Unemployed persons are defined as those who are Metropolitan Areas, OECD Publishing, Paris, http:// without work, are available for work, and have taken dx.doi.org/10.1787/9789264174108-en. active steps to find work in the last four weeks. The unemployment rate is defined as the ratio Figure notes between unemployed persons and labour force where the latter is composed of unemployed and employed 2.44-2.45: Values refer to the year 2014 with the exception of Australia, persons. Austria, Czech Republic, Switzerland 2013 and Slovenia 2011. Metropolitan labour figures are estimates based on data at TL3 level The employment rate is calculated as the ratio except for Chile, Mexico, Poland and Portugal where TL2 are used and between employment and working age population NOG for Canada. Figures for Australia and the United States are (aged 15-64 years). provided by the Australian Bureau of Statistics and U.S. Bureau of Values of employment and unemployment in the Labor Statistics respectively. Metropolitan working age population is estimated on municipal population for the last two available metropolitan areas are estimated by adjusting the Population Census. Swiss metropolitan areas are not included in the corresponding values of TL3 regions (see Annex C). figures due to lack of data on comparable years. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

82 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Employment and unemployment in metropolitan areas

2.44. Average employment rates in metropolitan areas, 2007 and 2014

2007 2014

% 90

Country (No. of cities)

1 2 http://dx.doi.org/10.1787/888933363415

2.45. Average unemployment rate change in metropolitan areas 2008-14 Percentage point change 25

-5

Country (No. of cities)

1 2 http://dx.doi.org/10.1787/888933363422

OECD REGIONS AT A GLANCE 2016 © OECD 2016 83 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Regional concentration of innovation related resources

In a knowledge-based economy, many drivers of Across the OECD area, the top 20% regions experienced a productivity are linked to innovation-related investments, slight decrease in their share of BERD from 47% to 45%. The such as skilled human capital or research and development bottom 20% accounted for less than 5%, albeit with a slight (R&D). While it is expected that R&D investments or increase over the period. While the pie continues to grow, patenting activity concentrate in the most productive the regions in the middle are taking a slightly larger share. regions so as to maximize the return, such a concentration The concentration of gross domestic R&D expenditures in of innovation-related resources in just a few regions may the top 20% regions has a somewhat different country limit the prospects for other regions to catch up if pattern. The concentration of the non-business R&D innovation does not “travel” across regions. Given this, a component is based largely on the location of high- common goal for regional development policy is to reduce performing universities and public laboratories that receive inter-regional disparities in these innovation factors by mainly public funding. Between 2000 and 2013, the boosting performance in the lagging regions. concentration in the top 20% regions increased in one- Certain innovation-related activities, such as patenting quarter of countries (6 out of 24) (Figure 2.48), most notably which represents invention, are highly concentrated in a few in Greece, where it more than doubled. In the remaining 18 regions. The top 20% TL3 regions account for over 50% or countries, the share in the top 20% regions declined, such more of total patenting volume in Slovenia, Japan, and as from 54% to 29% in Austria, from 58% to 33% in the Czech France, followed by Canada, the United Kingdom and the Republic and from 47% to 27% in Slovenia. Slovak Republic at somewhat under 50%. Between 1994-96 and 2011-13, the concentration in the top 20% has decreased in half of the countries (19 out of 28) with an increasing Source concentration noted particularly in Slovenia, and decreasing OECD (2015), OECD Regional Statistics (database), http:// concentration in the Czech Republic and New Zealand dx.doi.org/10.1787/region-data-en. (Figure 2.46). OECD (2015), OECD Patent Databases (database), http:// Business Enterprise R&D expenditure (BERD) illustrates the oecd.org/sti/inno/oecdpatentdatabases.htm. decisions by firms regarding the location and level of R&D investments to support innovation. While the volume of BERD continued to increase in the OECD area between 2000 Reference years and territorial level and 2013, the share in the top 20% TL2 regions has decreased over this period in 20 out of 24 countries (Figure 2.47). The 2.46: 1994-2013; TL3. countries with the highest share of BERD in the top 2.47: Business R&D 2000-13; TL2. 20% regions are the Slovak Republic, Switzerland, Poland, 2.48: Total R&D 2000-13; TL2. the United States, France and Hungary. Countries that experienced the largest increases in concentration in the top 20% regions include the Slovak Republic and Finland. Further information Those with notable decreases include the Netherlands and Switzerland. OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris, http:// dx.doi.org/10.1787/9789264239012-en. Definition OECD (2011), Regions and Innovation Policy, OECD Reviews of The top 20% regions are defined as those with the Regional Innovation, OECD Publishing, Paris, http:// highest value of the indicator until the equivalent of dx.doi.org/10.1787/9789264097803-en. 20% of the national population is reached. The same calculation is made for the bottom 20%. Patent data refers to Patent Co-operation Treaty (PCT) Figure notes

applications. Counts are based on the address of Estonia and Luxembourg are excluded from all figures as both consist of inventors (where the invention takes place) and not only one TL2 region. the address of the applicant. 2.46: Belgium, Denmark, Greece, Israel and the Netherlands are not Gross domestic spending on R&D is defined as the total included due to lack of data for comparable years. expenditure (current and capital) on R&D carried out by 2.47-2.48: Chile, Denmark, Iceland, Israel, Japan (included for Total R&D), all resident companies, research institutes, university Mexico, Norway, Switzerland (included for Business R&D) and Turkey and government laboratories, etc., in a country. are not included due to lack of data and or comparable years. For Business R&D, 231 regions and for Total R&D 229 regions with R&D intensity is defined as R&D expenditure over comparable data were available to calculate OECD-wide top and GDP, whether for Gross Domestic Expenditure on R&D bottom 20%. Last available year: Greece, Japan, Netherlands, Norway or a sector, such as Business Enterprise R&D. and Switzerland 2011. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

84 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional concentration of innovation related resources

2.46. Concentration of patents in the top 20% of TL3 regions, 1994-96 and 2011-13

Average 2011-13 Average 1994-96

% 70

1 2 http://dx.doi.org/10.1787/888933363434

2.47. Concentration of business R&D expenditure 2.48. Concentration of total R&D expenditure in the top 20% of TL2 regions, 2000 and 2013 in the top 20% of TL2 regions, 2000 and 2013

2013 2000 2013 2000

SVK GRC CHE SVK POL POL USA FRA OECD24 HUN FRA USA HUN ESP GRC NOR DEU OECD24 ESP GBR GBR PRT ITA CZE SWE DEU PRT ITA NOR FIN CZE AUT FIN KOR KOR SVN AUT JPN SVN NLD AUS AUS CAN CAN IRL SWE BEL BEK NLD IRL 0 10203040506070 0 10203040506070 % % 1 2 http://dx.doi.org/10.1787/888933363447 1 2 http://dx.doi.org/10.1787/888933363450

OECD REGIONS AT A GLANCE 2016 © OECD 2016 85 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Regions and venture capital

Entrepreneurship is an important driver of job creation, countries such as France, the United Kingdom and Canada competitiveness and economic growth. Countries are (Figure 2.51). Generally, the shares of VC are between 0.01% therefore increasingly implementing policies to attract and 0.5% of GDP, while in the top regions of Spain, the venture capital (VC) and business angels at the regional United States and Germany, those shares constitute level (OECD, 2011). To attract VC into regions with few VC 1.3% (Extremadura), 1.2% (California) and 0.8% (Berlin) firms and low levels of investment by non-local VC firms, respectively. In some cases though, the data can give a public policies generally attempt to create a favourable misleading VC financing picture. Those high regional environment for entrepreneurship. This focus can help to shares are not always driven by a vibrant entrepreneurial remove obstacles for a nascent start-up market, including ecosystem. In the case of Extremadura (Spain) or Prince outside established industry clusters, and facilitate the Edward Island (Canada), the top region in 2014 in their attraction of VC investments. respective countries, this is likely due to an unusual Evidence suggests that the allocation of VC is geographically volume of investment in a given year combined with a concentrated and plays a primary role in fostering relatively low volume of regional GDP. entrepreneurial communities. For example, in the United States, a strong concentration of VC in the top 20% regions has increased from 51% in 1995 to 76% in 2014, Sources largely driven by the dominance of a few metropolitan areas Venture Capital in cities: National Venture Capital with outstanding performance (Figure 2.49). Many of these Association (NVCA), Business R&D expenditures: OECD same metro areas are found in states with a high rate of (2015), OECD Regional Statistics (database), http://dx.doi.org/ business R&D intensity (business R&D share in gross 10.1787/region-data-en. domestic product [GDP]). Venture Capital in regions: United States: Pricewaterhouse Regional concentration of VC investment is striking in most Coopers/National Venture Capital Association MoneyTree™ countries (Figure 2.50). The top region in 3 of the 6 Report, Data: Thomson Reuters, Canada: Canadian Venture countries (United States, France and Canada) was host to Capital and Private Equity Association, Spain: Asociación almost half (between 48% and 49%) of all VC invested in the Española de Entidades de Capital Riesgo, Germany: Bundesverband respective country in 2014. Such top regions in countries Deutscher Kapitalbeteiligungsgesellschaften, United Kingdom: distance themselves from other regions in capturing British Private Equity and Venture Capital Association, France: national VC funds. Germany stands out for having a more Association Française des investisseurs pour la croissance. balanced pattern of VC investment across regions. The intensity of regional VC expressed as the share of regional GDP also highlights large regional disparities. Reference years and territorial level Overall, the top regions in Spain, the United States and Germany show a greater VC intensity than in other 2.49: Metropolitan areas in the map are defined according to the U.S. Office of Budget and Management definition of Metropolitan Statistical Areas. 2.50-2.51: TL2 level. All venture capital data refers to 2014 Definition and Business R&D to 2011. The top 20% regions are defined as those with the highest value of the indicator until the equivalent of Further information 20% of the national population is reached. Venture Capital: Private capital provided by specialised OECD (2015), OECD Science, Technology and Industry firms acting as intermediaries between primary sources Scoreboard 2015: Innovation for growth and society, OECD of finance (insurance, pension funds, banks, etc.) and Publishing, Paris, http://dx.doi.org/10.1787/sti_scoreboard- private start-up and high-growth companies whose 2015-en. shares are not freely traded on any stock market. OECD (2014), Entrepreneurship at a Glance 2014,OECD Business Expenditure on R&D (BERD), are R&D Publishing, Paris, http://dx.doi.org/10.1787/entrepreneur_aag- expenditures performed in the business sector that 2014-en. include both publicly and privately funded R&D. BERD OECD (2011), Financing High-Growth Firms: The Role of Angel intensity is expressed as the share of BERD in GDP. Investors, OECD Publishing, Paris, http://dx.doi.org/ 10.1787/9789264118782-en.

86 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regions and venture capital

2.49. BERD intensity by state (2011) and concentration of venture capital in metropolitan areas (2014) in the United States

2002

Share of total VC Business R&D as a % of GDP 2.5%-5% < 3.5% < 15% 1.5%-2.5% 2%-3.5% 1%-2% 5%-15% 0.5%-1.5% 0.5%-1% < 0.5% < 0.5%

1 2 http://dx.doi.org/10.1787/888933363461

2.50. Regional range in the share of total venture 2.51. Regional range in venture capital as share capital investments, of regional GDP, 2014 (TL2) 2014 (TL2) Country

2014 Extremadura USA ESP California 2013 Basque Country

2014 California FRA USA Île-de-France 2013 Massachusetts

2014 Berlin CAN DEU Ontario 2013 Berlin

2014 Languedoc-Roussillon ESP FRA Madrid 2013 Île-de-France

2014 London GBR GBR London 2013 East Midlands

2014 Prince Edward Island DEU CAN Bavaria 2013 n.a.

0102030405060 0 0.5 1.0 1.5 % % 1 2 http://dx.doi.org/10.1787/888933363471 1 2 http://dx.doi.org/10.1787/888933363482

OECD REGIONS AT A GLANCE 2016 © OECD 2016 87 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Regional differences in highly-skilled workers

The quality of human capital is central to increasing Portugal, with average annual growth ranging between 5% productivity, as the ability to generate and make use of and 7.7%. Poland, Portugal, Slovenia and Austria also innovation depends on, among other factors, the capacity experienced the highest average annual growth in the and skill level of the labour force. The proportion of the bottom 20%, with 7.1% per annum in Poland, followed by labour force with tertiary education is a common proxy for Portugal (6.6%) and Slovenia (5.4%) and Austria (5.4%). a region’s capacity to produce and absorb innovation. In contrast, the inter-regional gaps in terms of R&D Across OECD countries, large differences in the tertiary personnel do not show the same convergence trend as for educational attainment of the labour force existed in 2014, tertiary-educated workers. Between 2000 and 2013, the gap ranging from 18% in Italy to 45% in Israel. At the same time, between the top and bottom 20% regions for the rate of large disparities among regions within the same country R&D personnel per 1 000 employees increased in 12 out of can also be observed. The United States, Australia, Spain, 19 countries. Those countries experienced higher growth Turkey, the Czech Republic and Japan show the largest rates in the share of R&D personnel in the top 20% regions. regional disparities with gaps between the top and bottom An even more worrying trend is that the bottom 20% regions of 25 percentage points or more. Among the top regions actually showed declining values, not merely lower regions across the OECD are many country capitals. The growth rates, in many countries (Figure 2.54). Where gaps Australian Capital Territory (Australia) is the OECD region are narrowing, the bottom 20% regions are increasing R&D with the highest share of tertiary-educated workers personnel in the labour force at a faster rate. In the case of (64.6%), followed by the District of Columbia Ireland, Belgium, and the Slovak Republic, the leading (United States), Greater London (United Kingdom) and regions are decreasing their share of R&D personnel at a Tokyo (Japan) (Figure 2.52). faster pace than the bottom 20%. The drop in R&D Between 2000 and 2013 in all countries, both the top 20% personnel in the top 20% regions in Finland is likely due in and bottom 20% of regions experienced a growth in the part to the downsizing of the firm Nokia. share of their labour force with tertiary education (Figure 2.53). The average annual growth rates are relatively Source high in the majority of countries. Furthermore, in 22 out of 27 countries, the share in the bottom 20% regions grew OECD (2015), OECD Regional Statistics (database), http:// faster, reducing the gap in tertiary-educated workers dx.doi.org/10.1787/region-data-en. between the top and bottom 20% regions. Those countries with the highest increase of tertiary-educated workers in the top 20% regions were Italy, Poland, Slovenia and Reference years and territorial level

All figures refer to TL2 level. Year range: 2.52: 2014, 2.53: 2000-14, 2.54: 2000-13.

Definition Further information The labour force with advanced educational qualifications is defined as the labour force aged 15 OECD (2015), Education at a Glance 2015: OECD Indicators, and over that has completed tertiary educational OECD Publishing, Paris, http://dx.doi.org/10.1787/eag- programmes. Tertiary education includes both 2015-en. university qualifications and advanced professional programmes (ISCED 5 and 6). Figure notes R&D personnel includes all persons employed directly in R&D activities, such as researchers and those Estonia and Luxembourg are excluded in all figures as both consist of providing direct services such as R&D managers, only one defined TL2 region. administrators and clerical staff. Data are expressed 2.52: Norway is excluded due to lack of data and/or comparable years. in headcounts per 1 000 employees. 2.53: Australia, Chile, Denmark, Greece and Turkey are excluded due to The top 20% regions are defined as those with the lack of data and/or comparable years. highest value of the indicator until the equivalent of 2.54: Australia, Chile, Denmark, Estonia, France, Iceland, Israel, Japan, 20% of the national population is reached. Luxembourg, Mexico, New Zealand, Switzerland, Turkey, United Kingdom and United States are excluded due to lack of data and/or comparable years.

88 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional differences in highly-skilled workers

2.52. Regional range of labour force with tertiary educational attainment in TL2 regions, 2014

National value Regional value %

FIN ISL ITA IRL USA AUS ESP TUR CZE JPN GBR SVK FRA CHL MEX GRC NLD DNK HUN SWE PRT POL DEU AUT NZL CHE KOR ISR CAN BEL SVN

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2.53. Average annual change in the share 2.54. Average annual change in the share of the labour force with tertiary education of R&D personnel in the top in the top and bottom 20% regions, 2000-14 and bottom 20% regions, 2000-13

Average annual change in top 20% regions Top 20% regions Bottom 20% regions Average annual change in bottom 20% regions

ITA AUT POL ESP SVN PRT ITA AUT NOR IRL SVK SWE CZE HUN FIN KOR PRT GBR SVN CHE CZE HUN GRC CAN CAN POL ESP NLD GRC MEX KOR FRA ISR NLD NOR DEU BEL IRL USA SWE BEL NZL SVK DEU JPN FIN 0246810 -4 -2 0 2 4 % %

1 2 http://dx.doi.org/10.1787/888933363509 1 2 http://dx.doi.org/10.1787/888933363511

OECD REGIONS AT A GLANCE 2016 © OECD 2016 89 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Regional patterns of co-patenting

The percentage of regional patent applications with propensity to co-patent within the same region. Japan, co-inventors from another region, whether or not they Korea, Turkey, and the United States co-patent domestically belong to the same country, is an indicator of co-operation and show the lowest propensity to co-patent beyond activity between the two regions. national borders. By contrast, Estonia, Greece, Mexico and More than 70% of patents in OECD countries are applied for Chile – which have a low level of patenting in general – seem by two or more inventors. The share of co-patenting in the more oriented toward international co-operation total Patent Cooperation Treaty (PCT) applications can be (Figure 2.56). high for patenting leader countries (such as the Among the 40 regions with the highest number of patent United States and Germany), small economies (such as applications, different patterns of collaboration emerge. Iceland and Latvia) and emerging economies (such as Top patenting regions such as the region of Flanders India). For most of the countries, the share of patents with (Belgium) and Ontario (Canada) display a high share of co-inventors increased over the last 15 years, and most collaboration and are relatively more connected with other significantly in Estonia, Iceland, Portugal and Greece; while foreign hubs. The top ranking regions in Asian countries it decreased in Japan and Luxembourg (Figure 2.55). show a lower propensity to collaborate in patenting in The propensity to co-patent with co-inventors from the general and with foreign regions, exceptions being same TL3 region (average 50%) is higher than with co- Shanghai and Beijing. States in the United States show a inventorsfromotherregionsinthesamecountry relatively low share of international collaboration – given (average 29%) and from foreign regions (average 21%). their already large pool of domestic co-patenting partners – Slovenia, Turkey, Japan and Chile show the highest although it has increased since 1995-97 (Figure 2.57).

Source

Definition OECD (2015), OECD Regional Statistics (database), http:// A patent is an exclusive right granted for an invention, dx.doi.org/10.1787/region-data-en. which is a product or a process with industrial See Annex B for data, source and country-related metadata. applicability that provides, in general, a new way of doing something or offers a new technical solution to a problem (“inventive step”). A patent provides Reference years and territorial level protection for the invention to the owner of the patent. 2010-12 average. The protection is granted for a limited period, generally 20 years. TL3 regions, TL2 regions for Brazil, China, India and South Africa. Data refer to overall patent applications to Patent Co- operation Treaty (PCT) applications. Patent documents report the inventors (where the Further information invention takes place), as well as the applicants (owners), along with their addresses and country of OECD (2009), OECD Patent Statistics Manual, OECD Publishing, residence. Patent counts are based on the inventor’s Paris, http://dx.doi.org/10.1787/9789264056442-en. region of residence and fractional counts. If two or more inventors are registered on the patent document, the patent is classified as a co-patent. Figure notes

The number of foreign co-inventors is defined as the 2.57: TL2 regions; 2010-12 average increase or decrease compared to number of co-inventors that reside/work in a region 1995-97 average. The x- and y- axes are centred to the median among outside national borders. regions. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

90 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Regional patterns of co-patenting

2.55. Patent applications with co-inventors as a % 2.56. Share of co-patents by location of partners, of patents, average 2010-12 and 1994-96 TL3 regions, average 2010-12

2010-12 1994-96 Within region Within country Foreign region

USA SVN BEL TUR NLD JPN POL CHL FRA ESP DEU NZL OECD34 AUS ISL USA JPN NLD FIN FIN CHE SWE HUN GRC ISR CAN MEX KOR EST IRL KOR PRT OECD34 SWE NOR ESP ITA SVN GBR GBR IRL CZE CAN AUT ISL NZL FRA DNK SVK EST AUT ITA DEU NOR CHE CHL PRT MEX DNK SVK CZE AUS HUN GRC POL TUR ISR LUX BEL IND CHN LVA ZAF CHN BRA RUS IND BRA RUS LTU LTU ZAF LVA 0 10 20 30 40 50 60 70 80 90 0 20406080100 % % 1 2 http://dx.doi.org/10.1787/888933363521 1 2 http://dx.doi.org/10.1787/888933363536

2.57. Per cent of co-patents (X axis) and foreign collaborations (Y axis) in the top 40 regions with the highest patent applications, 2010-12

% foreign collaborations on total collaboration 80

Low High BEL 70 collaboration collaboration High connection High connection with foreign with foreign places places 60 CAN GBR

50 CHN SWE 40 DEU FRA DEU ITA 30 DEU USA FRA USA

20 USA Low USA High collaboration NLD collaboration Low connection Low connection 10 USA with foreign with foreign places CHN JPN and KOR places 0 55 60 65 70 75 80 85 % of co-patents on total patents

1 2 http://dx.doi.org/10.1787/888933363549

OECD REGIONS AT A GLANCE 2016 © OECD 2016 91 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS Patent activity in metropolitan areas

Metropolitan areas are the places where most inventions San Francisco and San Diego (United States) are among the take place. In 2011-13, 70% of all patent applications were metropolitan areas with the highest patent intensity per granted in metropolitan areas for the 19 OECD countries million inhabitants (above 1 000). where such data are available. The concentration of patent Metropolitan areas specialise in the Information and applications in metropolitan areas is above 70% both in Communications Technology (ICT) sector. In 2013, 41% of countries with a high level of patenting activity, such as patents granted in the 236 OECD metropolitan areas with Japan and United States, and in countries with low level of data were in the ICT sector. This was followed by health patenting activity, Australia, Chile and Mexico. In Norway care (15%), environment (9%), biotechnology (6%) and and Italy, on the other hand, metropolitan areas account nanotechnology (1%). Metropolitan areas in Estonia, for less than 40% of the country’s total patents (Figure 2.58). Finland and Sweden are among the most specialised in ICT In 2011-13, patent intensity (i.e. the number of patents per patents. Metropolitan areas in Denmark and the million inhabitants) in metropolitan areas was around 200, Netherlands display the largest share of patents in the a value more than double that of the rest of the OECD health sector compared with other countries, while economy (for the 19 OECD countries with data) metropolitan areas in Chile, Mexico and Germany show (Figure 2.59). Eindhoven (Netherlands), Shizuoka (Japan), higher shares in the environmental sector than metropolitan areas in other countries (Figure 2.60).

Definition Source

281 Metropolitan areas have been identified in OECD (2015), “Metropolitan areas”, OECD Regional 30 OECD countries according to the OECD-EU Statistics (database), http://dx.doi.org/10.1787/data-00531-en. methodology that identifies metropolitan areas on OECD (2015), OECD Patent Statistics (database), http:// the basis of densely populated cities and their dx.doi.org/10.1787/patent-data-en. commuting zones (travel to work journeys) to reflect the economic geography of the population’s daily commuting patterns (see Annex A for details). Reference years and territorial level A patent is an exclusive right granted for an invention, The functional urban areas (FUA) have not been identified which is a product or a process with industrial in Iceland, Israel, New Zealand and Turkey. The FUA of applicability that provides, in general, a new way of Luxembourg does not appear in the figures since it has a doing something, or offers a new technical solution to population below 500 000 inhabitants. a problem (“inventive step”). A patent provides protection for the invention to the owner of the patent. Data on patent activity in metropolitan areas are available The protection is granted for a limited period, only for 19 OECD countries containing 236 metropolitan generally 20 years. areas. Data refer overall to patent applications made under the Patent Co-operation Treaty (PCT). Further information Patent documents report the inventors (where the invention takes place), as well as the applicants OECD (2012), Redefining “Urban”: A New Way to Measure (owners), along with their addresses and country of Metropolitan Areas, OECD Publishing, Paris, http:// residence. Patent counts are based on the inventor’s dx.doi.org/10.1787/9789264174108-en. region of residence and fractional counts. OECD (2015), OECD Science, Technology and Industry The patent intensity is the ratio between the number Scoreboard 2015: Innovation for growth and society, OECD of patent applications and the metropolitan area’s Publishing, Paris, http://dx.doi.org/10.1787/sti_scoreboard- population. 2015-en. Patents are allocated to different fields (ICT, health, climate change mitigation, biotechnology or Figure notes nanotechnology) on the basis of their International Patent Classification (IPC) codes. 2.58-2.60: Figures refer to three-year average 2011-13. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

92 OECD REGIONS AT A GLANCE 2016 © OECD 2016 2. REGIONS AS DRIVERS OF NATIONAL COMPETITIVENESS

Patent activity in metropolitan areas

2.58. Per cent of patent applications in metropolitan 2.59. Patent intensity in metropolitan areas areas and the rest of the country, and the rest of the country, average 2011-13 average 2011-13 Patent applications per million inhabitants

Metropolitan areas Rest of the country Metropolitan areas Rest of the economy Country (No. of cities) Country (No. of cities) MEX (33) SWE (3) AUS (6) FIN (1) CHL (3) NLD (5) JPN (36) JPN (36) USA (70) DNK (1) CAN (9) DEU (24) OECD19 (236) USA (70) FRA (15) FRA (15) EST (1) OECD19 (236) NLD (5) NOR (1) SWE (3) AUT (3) ESP (8) BEL (4) CAN (9) DNK (1) AUS (6) BEL (4) ITA (11) PRT (2) ESP (8) AUT (3) EST (1) DEU (24) PRT (2) FIN (1) CHL (3) ITA (11) MEX (33) NOR (1) 0 200 400 600 0 20406080100 % 1 2 http://dx.doi.org/10.1787/888933363552 1 2 http://dx.doi.org/10.1787/888933363567

2.60. Per cent of patent applications in metropolitan areas by sector, average 2011-13

ICT Health Environment Biotechnology Nanotechnology Other

% 100

Country (No. of cities)

1 2 http://dx.doi.org/10.1787/888933363573

OECD REGIONS AT A GLANCE 2016 © OECD 2016 93

3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT

Subnational government spending

Subnational government spending by type

Subnational government spending by economic function

Spending responsibilities across levels of government

Subnational government investment

Subnational government revenue

Subnational government debt

Challenges for infrastructure investment at subnational level

The data of Chapter 3 are derived mainly from the OECD National Accounts, harmonised according to the new standards of the System of National Accounts (SNA) 2008, with the exception of Chile, Japan and Turkey, which are still under SNA 1993. Eurostat and International Monetary Fund (IMF) data were also used. General government includes four sub-sectors: central/federal government and related public entities; federated government (“states”) and related public entities; local government, i.e. regional and local governments, and related public entities; and social security funds. Data are consolidated within the four sub-sectors, as well within each subsector (neutralisation of financial cross-flows). Subnational governments (SNG) are defined as the sum of state government (relevant only for countries having a federal or quasi-federal system of government) and local (regional and local) governments. For Australia and the United States, there is no breakdown available at subnational level between local and state government data.

OECD REGIONS AT A GLANCE 2016 © OECD 2016 95 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT Subnational government spending

Subnational government (SNG) expenditure stood at SNG spending responsibilities have changed over the past USD 6 450 per capita on average in the OECD area, 20 years, notably as a result of decentralisation processes accounting for 17% of gross domestic product (GDP) and that have transferred competences to the subnational level 40% of total public expenditure in the OECD in 2014 in sectors such as education, health, social protection, (Figure 3.1). economic development, urban and spatial planning, etc. In Among OECD countries, the SNG share of total public Spain, for example, the weight of SNG government expenditure varied from less than 10% in Greece and expenditure in GDP increased by more than 5 percentage Ireland, to almost 80% in Canada. SNG spending may vary points between 1995 and 2014, while the weight of total according to whether the country is federal or unitary, its public expenditure increased by more than 13 percentage size and territorial organisation, the level of decentralisation points. Some OECD countries, however, have recentralised and the nature of responsibilities for certain sectors and thus the share of SNG expenditure has decreased over (Figure 3.1). the last 20 years and especially since the crisis e.g. Ireland, Hungary (Figure 3.3). In federal countries, SNG expenditure reached USD 8 500 per capita, corresponding to 19% of GDP and 49% of public The share of SNG expenditure as an indication of spending expenditure. In Canada the value was almost USD 13 800 per autonomy should be interpreted with caution. While it often capita, corresponding to 31% of national GDP. In federal provides a valuable macroeconomic overview of the level countries, the share of expenditure carried out by the local of decentralisation, it can also lead to an overestimation government compared to that of state government varies: of SNG expenditure autonomy. In fact, it does not always while half of SNG expenditure is carried out by municipalities assess the real degree of decision-making power and action in Austria and 38% in Germany, municipalities represented that SNGs have in terms of expenditure. In some countries, only 14% of SNG expenditure in Mexico in 2014 (Figure 3.2). the “spending autonomy” of SNG can be restricted because of mandatory spending (acting as “paying agent” for In unitary countries, local government expenditure is lower example for teachers’ salaries or social security benefits), than in federal countries, representing on average regulatory constraints or budget norms. USD 4 330 per capita, or 13% of GDP and 29% of public expenditure. However, while in Chile, Greece, Ireland, New Zealand and Turkey local governments have limited Source competencies and spending capacity, in Japan and European Nordic countries local expenditure is an OECD (2016), National Accounts Statistics (database), http:// important share of public expenditure. In Denmark, for dx.doi.org/10.1787/na-data-en. example, SNG expenditure amounts to USD 16 560 per OECD (2016), “Subnational Government Structure and capita, corresponding to 36% of GDP and 64% of public Finance”, OECD Regional Statistics (database), http:// expenditure due to the fact that municipalities administer dx.doi.org/10.1787/05fb4b56-en. a number of social security transfers (Figure 3.2). See Annex B for data sources and country-related metadata.

Reference years and territorial level Definition 2014: National Economic Accounts; Levels of government; General government includes four sub-sectors: 2013: Chile, Mexico and New Zealand; 2012: Australia; central/federal government and related public 2011: Turkey. entities; federated government (“states”) and related public entities; local government, i.e. regional and local governments, and related public entities; and Further information social security funds. Data are consolidated within OECD (2016), “Subnational Governments in OECD the four sub-sectors. Subnational government is Countries: Key data” (brochure), www.oecd.org/gov/ defined as the sum of state governments and local/ regional-policy/Subnational-governments-in-OECD- regional governments. Countries-Key-Data-2016.pdf. Expenditure comprises: “current expenditure” and “capital expenditure” (see Annex B for a detailed definition). Figure notes The OECD averages are presented as the weighted 3.1-3.2: OECD9 and OECD25 refer to federal and unitary countries, average of the OECD countries for which data are respectively. available, unless otherwise specified (i.e. unweighted 3.3: Australia 1995-2012; Mexico 2003-2013; New Zealand 1995-2013; average, arithmetic mean, OECD UWA). Data in USD Iceland 1998-2014; Netherlands 1996-2014; Ireland 2005-2014. No use Purchasing Power Parities. data for Chile and Turkey due to lack of time-series. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

96 OECD REGIONS AT A GLANCE 2016 © OECD 2016 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT

Subnational government spending

3.1. Subnational government expenditure as a percentage of GDP and total public expenditure, 2014 Subnational expenditure as a share of total public expenditure (%) 90

CAN 80

70 DNK CHE 60

SWE MEX USA OECD9 ESP 50 AUS DEU KOR BEL JPN FIN 40 OECD34 WA AUT NOR POL EU28 ISL 30 CZE OECD34 UWA EST GBR ITA NDL OECD25 SVN 20 ISR SVK HUN LUX FRA CHL TUR PRT 10 IRL GRC NZL

0 0 5 10 15 20 25 30 35 40 Subnational expenditure as a share of GDP (%)

Federal countries : dark red markers 1 2 http://dx.doi.org/10.1787/888933363584

3.2. Public expenditure per capita 3.3. Changes in subnational expenditure by level of government between 2014 and 1995, as a share of total public (USD PPP), 2014 expenditure and of GDP (percentage points)

Local government 1995-2014 as a % of total public expenditure State government State and local government 1995-2014 as a % of GDP Central government and social security ESP LUX SWE NOR CAN DNK POL AUT DNK BEL FIN FIN SWE DEU FRA SVN NDL ITA USA BEL DEU CZE CHE ISL SVK IRL FRA ITA GRC EU28 AUS CAN EU28 GBR MEX OECD9 CHE OECD34 AUS PRT JPN NZL SVN USA ESP AUT OECD25 ISL PRT ISR NZL ISR GBR GRC KOR CZE NOR HUN EST SVK LUX EST NDL KOR POL HUN TUR IRL CHL JPN MEX -15 -10 -5 0 5 10 15 0 5 000 10 000 15 000 20 000 25 000 30 000 35 000 40 000 45 000 percentage point USD PPP

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OECD REGIONS AT A GLANCE 2016 © OECD 2016 97 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT Subnational government spending by type

The importance of subnational governments (SNGs) in the In 2014, SNGs accounted for 50% of public procurement in economy is particularly evident when considering their the OECD, 61% in federal countries and 38% in unitary role as employers. Staff spending is the largest expense in countries (Figure 3.6). Among this category, intermediate SNG budgets, representing on average 36% of expenditure consumption expenditure and gross fixed capital formation in the OECD area, and ranging from less than 20% in represented respectively 22% and 11% of SNG spending (see New Zealand to more than 50% in Norway (Figure 3.4). High section on investment for further details). budget shares for staff spending may reflect the fact that “Spending indicators” should be interpreted with caution. SNGs in several countries have the responsibility, delegated They tend to overestimate the level of decentralisation, as from the central government, for the payment of public SNG spending autonomy is often restricted by mandatory workers’ salaries, such as teachers, medical staff or social expenses in the case of shared or delegated competences, workers. On average in the OECD area, SNGs undertook 63% regulatory constraints, centrally imposed standards on local of public staff expenditure in 2014. This average masks public service delivery (quantity and quality, cost, etc.) or on different situations between federal countries (76%) and public procurement, civil service obligations or budget unitary countries (45%), from less than 10% in Greece and discipline (e.g. budget balance targets). In many cases, SNGs New Zealand to more than 84% in Switzerland and Canada act simply as paying agents on behalf of the central (Figure 3.5). government, for example for the payment of public staff SNGs also play a significant role in public procurement wages or social benefits, with little or no decision-making through the purchase of goods and services for intermediate power and room for manoeuvre (see Annex D for details). consumption (equipment and supplies, maintenance and repairs, energy, communication and information Source technology, consulting, etc.) and the commissioning of public works, often to local small and medium-enterprises. OECD (2016), National Accounts Statistics (database), http:// dx.doi.org/10.1787/na-data-en. OECD (2016), “Subnational Government Structure and Finance”, OECD Regional Statistics (database), http:// Definition dx.doi.org/10.1787/05fb4b56-en. See Annex B for data sources and country-related metadata. General government includes four sub-sectors: central/federal government and related public entities; federated government ("states”) and related Reference years and territorial level public entities; local government, i.e. regional and local governments, and related public entities; and National Economic Accounts 2014; Levels of government; social security funds. Data are consolidated within Chile, Mexico and New Zealand 2013; Australia 2012; the four sub-sectors. Subnational government is Turkey 2011. Data by type of expenditure are not available defined as the sum of state governments and local/ for Australia and Chile. regional governments. Expenditure comprises: “current expenditure” and Further information “capital expenditure” (see Annex B for a detailed definition). OECD (2016), “Subnational Governments in OECD Countries: Key data” (brochure), www.oecd.org/gov/ Public procurement expenditure is defined as the regional-policy/Subnational-governments-in-OECD- sum of intermediate consumption, gross fixed capital Countries-Key-Data-2016.pdf. formation and social transfers in kind via market producers. Figure notes The OECD averages are presented as the weighted average of the OECD countries for which data are 3.4-3.6: OECD8 and OECD24/25 refer to federal and unitary countries, available, unless otherwise specified (unweighted respectively. average, arithmetic mean, OECD UWA). 3.6: Other category includes taxes and financial charges. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

98 OECD REGIONS AT A GLANCE 2016 © OECD 2016 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT

Subnational government spending by type

3.4. Breakdown of subnational government expenditure by type, 2014

Compensation of employees Intermediate consumption Social expenditure Subsidies & other current transfers Capital expenditure Other % 100

1 2 http://dx.doi.org/10.1787/888933363610

3.5. Subnational staff expenditure as a share 3.6. Subnational government procurement of total public staff expenditure, 2014 (%) as a share of total public procurement, 2014 (%)

Subnational government Subnational government Central government and social security Central government and social security

CAN CAN CHE ESP BEL ITA DEU CHE ESP SWE JPN USA USA FIN OECD8 DNK SWE OECD8 FIN MEX DNK POL MEX OECD32 OECD32 EU28 NDL AUT NOR NOR KOR BEL AUT CZE POL DEU EU28 ISL CZE OECD24 OECD24 KOR ISL GBR ITA SVN EST FRA GBR JPN SVK EST SVN NDL FRA PRT HUN HUN LUX IRL PRT TUR ISR ISR TUR LUX IRL SVK GRC NZL NZL GRC 020406080100 020406080100 % %

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OECD REGIONS AT A GLANCE 2016 © OECD 2016 99 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT Subnational government spending by economic function

The breakdown of subnational expenditure by economic Public order, safety and defence expenditures accounted function provides a measure of subnational governments for around 7% of SNG expenditure in 2013. This category (SNGs) role in economic functions. Education represents includes mainly local and regional police services, fire- the largest sector in the SNG expenditure, on average 25% protection, civil protection and emergency services. of SNG expenditure in the 28 OECD countries where data Housing and community amenities represented on average were available in 2013. In the Slovak Republic, Slovenia, around 3% of SNG expenditure in the OECD in 2013, and Estonia, Israel, Iceland and the Czech Republic, spending more than 7% in the Slovak Republic, Hungary, Luxembourg, on education exceeded 30% of local budgets, and in the Portugal, France and Ireland. This function comprises Slovak Republic was 44% (Figure 3.7). various sub-sectors such as supply of potable water, public Health is the second highest budget, accounting for 17% of lighting, urban heating, housing (construction, renovation SNG expenditure. It exceeded 23% of SNG budgets in the and acquisition of land) and urban planning and facilities. United States, Spain, Austria, Denmark, Sweden, Finland Recreation, culture and religion accounted for 2.9% of SNG and reached 47% in Italy. expenditure in the OECD in 2013, but more than 10% in Excluding general public services (15% of SNG spending), Estonia, Israel, Luxembourg and Iceland. In Iceland in the third largest subnational budget item is social particular, it reached 16.4% of local budget as culture is expenditure. This category, which includes current and considered as a driving force for economic and social capital social expenditure, represented around 14% of SNG development. expenditure in 2013, ranging between 24% and 33% in Lastly, environment protection accounted for 2.6% of OECD Germany, Finland, Norway, Sweden, Japan and the SNG expenditure, exceeding 10% in the Netherlands, United Kingdom. Social services are often provided by the Ireland, Luxembourg and Greece. municipal level, perceived to be closer to the needs of citizens, or delegated and outsourced through contracts to the private sector. In federal countries, the regional level Source can also play an important role in social protection (Austria, Germany and Belgium). OECD (2016), National Accounts Statistics (database), http:// Economic affairs expenditure (transport, communication dx.doi.org/10.1787/na-data-en. and economic interventions, etc.) represented almost 14% OECD (2016), “Subnational Government Structure and of OECD SNG expenditure in 2013, and more than 19% in Finance”, OECD Regional Statistics (database), http:// France, Korea, the Czech Republic and Ireland. dx.doi.org/10.1787/05fb4b56-en. See Annex B for data sources and country-related metadata. See Annex D for details of allocation of competencies Definition across levels of government. General government includes four sub-sectors: central/federal government and related public Reference years and territorial level entities; federated government ("states”) and related public entities; local government, i.e. regional and 2013: National Economic Accounts; levels of government. local governments, and related public entities; and COFOG data are not available for Australia, Canada, Chile, social security funds. Data are consolidated within Mexico, New Zealand and Turkey. For the United States, the four sub-sectors. Subnational government is data showed in the function “Housing and community defined as the sum of state governments and local/ amenities” include the “environment protection” function regional governments. data. Expenditure (current and capital) by economic function follows the Classification of the ten Functions of Government (COFOG): general public Further information services; defence; public order and safety; economic affairs; environmental protection; housing and OECD (2016), “Subnational Governments in OECD community amenities; health; recreation, culture and Countries: Key data” (brochure), www.oecd.org/gov/ religion; education; and social protection. regional-policy/Subnational-governments-in-OECD- Countries-Key-Data-2016.pdf. The OECD averages are presented as the weighted average of the OECD countries for which data are available, unless otherwise specified (i.e. unweighted Figure note average, arithmetic mean, OECD UWA). Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

100 OECD REGIONS AT A GLANCE 2016 © OECD 2016 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT

Subnational government spending by economic function

3.7. Breakdown of subnational government expenditure by economic function, 2013 (%)

Education Health General public services Social protection Economic affairs Recreation, culture and religion Housing and community amenities Environment Public order, safety and defence

SVK

SVN

EST

ISR

ISL

CZE

BEL

USA

NDL

GBR

OECD6

POL

KOR

CHE

OECD28

NOR

OECD28 UWA

DEU

SWE

OECD22

ESP

LUX

JPN

FIN

AUT

HUN

PRT

FRA

IRL

GRC

DNK

ITA

0102030405060708090100 % 1 2 http://dx.doi.org/10.1787/888933363640

OECD REGIONS AT A GLANCE 2016 © OECD 2016 101 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT Spending responsibilities across levels of government

The subnational spending by sector provides a standard in 2013 (Figure 3.8 panel B). Health remains a centralised measure of the distribution of spending responsibilities responsibility in several countries, such as Greece, Ireland, among the different levels of government in a country. Israel, Luxembourg, Slovak Republic, France or the However, spending indicators should be interpreted with United Kingdom. Health competences fall more often caution, as they tend to overestimate the level of under the responsibility of central government or social decentralisation. Subnational governments (SNGs), for security bodies and SNGs have no role, or a limited one. At example, may be responsible for a certain economic the other end of the spectrum, SNG health spending as a function but not have full autonomy in exercising them share total health spending exceeds 60% in Italy, Spain, (see Annex D for details). Switzerland and the Nordic countries. Wide responsibilities Education is a shared competency across levels of for planning, organising, delivering and financing government. As a share of total public spending on healthcare services and infrastructures are decentralised to education, SNG expenditure on education represented 51% the municipal level (primary care centres) but especially to on unweighted average in the OECD in 2013 but above this the regional level (hospitals, specialised medical services). average in 11 countries (Figure 3.8, panel A). In most SNG accounted for 35% of public spending on economic countries, SNGs are responsible for construction and affairs on unweighted average in the OECD in 2013, more maintenance of educational infrastructures and the than 50% in Spain, Switzerland, and 66% in the financing of school-related activities, commonly for the United States (Figure 3.8 panel C). Transport is the main primary level schools (there are however some exceptions, component of this area, representing 75% of economic e.g. in Ireland or in Greece where education is provided by affairs expenditure on unweighted average (in 18 OECD central government entities) and frequently also for the countries for which data are available). This sector secondary level schools. In other countries, SNGs are also encompasses a wide range of activities from the definition in charge of the payment of salaries for administrative and of policies, regulations and standards, to the financing, technical staff and teachers. In this case, SNG spending construction, maintenance and administration. Such power is limited: financed through earmarked transfers, activities can cover transport networks, facilities and they act more as paying agents with little control over their services in various sub-sectors and at various geographic budget in an area regulated by the central government scales (see Table D.1 in Annex D). level. By contrast, in Belgium, Switzerland, the SNG social expenditure corresponded to 15% of total public United States, Germany and Spain, SNG educational social spending on unweighted average in the OECD expenditure represents more than 75% of public spending (Figure 3.8 panel D). In the majority of OECD countries, in this sector. They are all federal countries, with federated social protection and benefits are mainly provided by the states having a high level of autonomy in educational central government, social security bodies or by insurance matters, including vocational teaching and higher institutions. Only Denmark stands out from the other education (universities). Finally, in some countries, countries as local governments are responsible for the education is decentralised not at SNG level but directly at administration of cash benefits. However, in this area, the level of education institutions, which may be there is a significant disconnection between the large share independent special-purpose governance, (e.g. schools of decentralised social expenditures and the real power of districts in the United States). Danish municipalities over them as social protection In the health sector, SNG expenditure represented 25% of schemes are largely determined by regulations and public health spending on unweighted average in the OECD standards set at the central level.

102 OECD REGIONS AT A GLANCE 2016 © OECD 2016 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT

Spending responsibilities across levels of government

3.8. Subnational expenditure as a share of total public expenditure by economic function, 2013 (%) Panel A. Education Panel B. Health

ESP CHE DEU ESP USA SWE CHE ITA BEL DNK SWE FIN JPN AUT NOR USA ISL POL EST NOR FIN OECD28 UWA OECD28 UWA EST KOR EU28 POL JPN NDL KOR AUT SVN CZE PRT DNK DEU GBR CZE EU28 HUN SVK NDL SVN BEL FRA GBR ISR FRA ITA ISL HUN SVK PRT LUX LUX ISR IRL IRL GRC GRC 0255075100125 0 20406080100 % %

Panel C. Economic affairs (including transport) Panel D. Social protection

USA DNK DEU SWE CHE KOR ESP CHE JPN NOR BEL BEL POL DEU NDL GBR FRA JPN KOR ISL AUT FIN ITA NDL OECD28 UWA OECD28 UWA SWE AUT EU28 EU28 CZE USA DNK ESP FIN FRA EST POL PRT ISR NOR HUN GBR IRL ISL EST IRL SVN SVK CZE ISR ITA LUX SVK HUN PRT SVN GRC GRC LUX 0 20406080 0 102030405060 % %

OECD REGIONS AT A GLANCE 2016 © OECD 2016 103 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT

Spending responsibilities across levels of government

SNGs are key public actors in housing and community Netherlands). It is also often outsourced to agencies, amenities. Their expenditure amounted to 72% of public external entities or private providers through public- spending in this area on unweighted average in the OECD private partnership contracts (e.g. in France). in 2013 (Figure 3.8 Panel E). In this field, SNGs play a major As a share of total public spending, subnational role in Belgium, Estonia, Spain, Switzerland and Norway, expenditure dedicated to recreation, culture and religion representing more than 90% of public spending. In Belgium amounted 62% on unweighted average in the OECD for example, social housing was decentralised entirely to countries, exceeding 85% in the United States, Switzerland, the regions in 1980, also involving a variety of providers Germany, Japan and Belgium (Figure 3.8 Panel G). By such as municipalities, public companies, foundations, co- contrast, central government remains the main public operatives and non- for profit organizations. In the social funder in this area in Ireland, Hungary, the Slovak Republic housing sector, there has been a widespread privatisation or the United Kingdom. process, which reduced SNG involvement, in particular in In most OECD countries, public order and safety functions central and eastern European countries. remain mainly the central government’s responsibility. The share of SNGs in total public environmental SNG expenditure represents only 25% of public spending in expenditure is also sizable, reaching 68% in the OECD on a this area on unweighted average (Figure 3.8 Panel H). unweighted average in 2013 (Figure 3.8 Panel F). It confirms However, federal countries, such as Switzerland, Germany, the key role of SNGs in this field, especially in Portugal, the United States, Belgium and Spain record particularly France, Netherlands and Spain, where subnational high ratios, as well Japan among the unitary countries. spending represented more than 85% of total public spending in 2013. In some sectors (e.g. waste, sewerage, Source parks and green spaces, see Table D.1 in Annex D), the competence is almost fully devolved to local governments OECD (2016), National Accounts Statistics (database), http:// or dedicated functional bodies (e.g. waterboards in the dx.doi.org/10.1787/na-data-en. OECD (2016), “Subnational Government Structure and Finance”, OECD Regional Statistics (database), http:// dx.doi.org/10.1787/05fb4b56-en. See Annex B for data sources and country-related metadata. Definition See Annex D for details of allocation of competencies General government includes four sub-sectors: across levels of government. central/federal government and related public entities; federated government ("states”) and related Reference years and territorial level public entities; local government i.e. regional and local governments and related public entities; and 2013: National Economic Accounts; levels of government. social security funds. Data are consolidated within COFOG data are not available for Australia, Canada, Mexico, the four sub-sectors. Subnational government is Chile, New Zealand and Turkey. For the United States, data defined as the sum of state governments and local/ in the function “housing and community amenities” regional governments. include the “environment protection” function data. Expenditure (current and capital) by economic function follows the Classification of the ten Further information Functions of Government (COFOG): general public services; defence; public order and safety; economic OECD (2016), “Subnational Governments in OECD affairs; environmental protection; housing and Countries: Key data” (brochure), www.oecd.org/gov/ community amenities; health; recreation, culture and regional-policy/Subnational-governments-in-OECD- religion; education; and social protection. Countries-Key-Data-2016.pdf. The OECD averages are presented as the weighted Figure notes average of the OECD countries for which data are available, unless otherwise specified (i.e. unweighted 3.8: OECD average is unweighted. The total of public spending is non- average, arithmetic mean, OECD UWA). consolidated. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

104 OECD REGIONS AT A GLANCE 2016 © OECD 2016 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT

Spending responsibilities across levels of government

3.8. Subnational expenditure as a share of total public expenditure by economic function, 2013 (%) (Cont.) Panel E. Housing and community amenities Panel F. Environment protection

BEL ESP EST NDL ESP FRA CHE PRT NOR JPN SWE ITA AUT ISR DEU CZE PRT CHE IRL DEU KOR EU28 NDL KOR GRC POL FRA POL GRC OECD28 UWA OECD27 UWA HUN IRL CZE NOR ITA HUN SVK SVN SVN BEL ISR SWE EU28 LUX ISL GBR JPN DNK FIN SVK DNK AUT USA EST LUX ISL GBR FIN 0 20406080100 0 20406080100 % %

Panel G. Recreation, culture and religion Panel H. Public order and safety

BEL CHE JPN DEU DEU USA CHE JPN USA BEL ESP ESP NDL GBR POL KOR FRA EU28 SWE OECD28 UWA CZE FRA AUT FIN EU28 NDL ISL NOR KOR SWE OECD28 UWA AUT PRT PRT NOR ITA FIN POL LUX CZE ITA DNK EST IRL DNK ISL GRC LUX ISR SVN SVN ISR GBR SVK SVK HUN HUN EST IRL GRC 0 20406080100 0 20406080100 % % 1 2 http://dx.doi.org/10.1787/888933363655

OECD REGIONS AT A GLANCE 2016 © OECD 2016 105 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT Subnational government investment

In most OECD countries, subnational governments (SNGs) stopped in 2013 but the investment has not recovered since have a key role in public investment. In 2014, they carried and has even slightly declined in the OECD in 2014. out around 59% of public investment in the OECD area. This Between 2007 and 2014, SNG investment decreased in the ratio tends to be higher in most federal countries where it OECD (average of -0,5% per year in real terms, totalling - combines investments by the states and by local 3,7% over the period). It contracted sharply in Ireland, Iceland, governments. In 2014, more than 70% of public investment Turkey (2007-11), Spain and Greece. However, not all OECD was made by SNGs in Germany, Australia, Mexico, Japan, countries followed this trend (Figure 3.11). Belgium, up to 95% in Canada (Figure 3.9). Economic affairs were the priority sector for SNG SNG investment represented 1.9% of gross domestic investment in 2013, accounting for 39% of SNG investment product (GDP) in the OECD in 2014 (total public investment on average in the OECD. Under this heading are transport, was around 3.2% of GDP) a share that was above 2.8% of GDP communications, economic development, energy, in Korea, Japan and Canada and less than 1% of GDP in Chile, construction, etc. Transport systems and facilities make Greece, Ireland, the Slovak Republic, the United Kingdom the bulk of investment in this category (around three- and Portugal (Figure 3.9). quarters). It comprises construction of roads, railways, Per capita SNG investment averaged around USD 730 in 2014, water transport, air transport and airports, pipeline and compared to USD 510 for the central government and social other transport systems such as funiculars, cable cars, etc. security sectors. It ranges from USD 57 in Chile to almost In Greece, Japan, Portugal and Ireland, investment in USD 1 490 in Canada, with high values of SNG investment per economic affairs represented more than 45% of SNG capita (above USD 1 000) found also in Australia, Japan, investment in 2013 (Figure 3.12). Switzerland, Norway and Luxembourg (Figure 3.10). The second priority sector for SNG investment in 2013 was In federal countries, local government investment per education: 22% of SNG investment was made in education capita tends to be smaller than those of state governments, for new construction and major building improvements of except in Austria and Germany where it is more balanced elementary, secondary and high schools, universities, adult (there is no breakdown data for the United States and vocational training centres, lodging and transport for pupils Australia). In unitary countries, the local government role and students, etc. SNG educational infrastructure in public investment is a little less pronounced than in investment was above 25% in Norway, Luxembourg, Israel federal countries, in particular in countries such as Chile, and the United States, up to 53% in the United Kingdom. Greece, Estonia or the Slovak Republic. There are, however, Infrastructure in general public services represented 9% of several countries, such as Japan, France and the SNG investment in 2013 but more than 25% in Sweden, Czech Republic where local governments played a crucial Hungary, Switzerland and Belgium. This category role in public investment in 2014 with a share of SNG comprises mainly construction and improvement of public investment in public investment above the OECD average. buildings. In many OECD unitary countries, and typically in the least- The fourth priority area of SNG investment in 2013 was decentralised countries, investing is the main function of housing and community amenities which represented local governments. In fact, having little competencies in almost 9% of SNG investment. This sector comprises key current spending areas, they tend to implement major construction and remodelling of housing, including national investment projects. Investment accounts for acquisition of land, potable water supply, street lighting, etc. more than 25% of local government expenditure in Investment in that area exceeded 14% in Slovenia, France, Hungary, Turkey, Slovenia, Luxembourg and New Zealand, Italy, Ireland and up to 29.5% in the Slovak Republic in 2013. as compared to 11% on average in the OECD. In a great number of countries, SNG investment was Environmental infrastructure reached almost 7% of SNG particularly robust in the early years of the global financial expenditure on average in the OECD in 2013. The share is crisis due to the involvement of SNG in stimulus plans and above 15% in 9 countries and exceeded 20% in Slovenia and strong support from national governments. However, the Hungary. deepening of the social and economic crisis, as well as the The “other” category represented around 15% of SNG adoption from 2010 onwards of national and subnational investment in 2013. It comprises investment in recreation budget consolidation measures put severe strain on SNG and culture facilities, theatres, museums, concert and finance. In a majority of countries, public investment was cut exhibitions halls, libraries, heritage, zoological and back. Used as a budgetary adjustment variable, investment botanical gardens, provision of facilities for religious and ultimately declined steeply across OECD countries. The fall other community services etc.

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Subnational government investment

3.9. Subnational government investment as a % of GDP and public investment, 2014 Subnational investment as a share of public investment (%) 120

100 CAN BEL

80 JPN MEX AUS DEU CHE ESP OECD9 CZE 60 OECD34 FRA ISR USA ITA EU28 POL FIN KOR AUT NLD OECD25 SVN PRT SWE ISL LUX DNK HUN 40 GBR TUR IRL NOR NZL SVK EST

20 GRC CHL

0 00.511.522.533.5 Subnational investment as a share of GDP (%)

1 2 http://dx.doi.org/10.1787/888933363662

3.10. Public investment by level of government, 3.11. Annual average change in subnational 2014 (USD PPP per capita) government investment between 2007 and 2014

Local SVN State government DNK State and local government SWE Central government & social security ISR CZE LUX BEL NOR FIN SWE HUN USA DNK JPN CHE POL KOR DEU FIN CHE EU28 NOR NDL LUX CAN SVN AUT FRA SVK EST CAN AUS AUS JPN NZL NZL OECD24 AUT ISL MEX OECD9 FRA HUN OECD33 OECD34 GBR CZE NDL OECD25 OECD9 GRC BEL EU28 GBR USA POL KOR DEU ITA IRL PRT SVK EST ITA ESP GRC ISR ESP PRT TUR TUR ISL CHL IRL MEX -20 -15 -10 -5 0 5 10 0 500 1 000 1 500 2 000 2 500 3 000 3 500 % USD PPP

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OECD REGIONS AT A GLANCE 2016 © OECD 2016 107 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT

Subnational government investment

Healthcare investment (hospitals, local health centres, Source specialised medical and paramedical service centres, maternity and nursing centres, heavy medical equipment) OECD (2016), National Accounts Statistics (database), http:// are particularly significant in Denmark and Sweden while dx.doi.org/10.1787/na-data-en. investment in public order and safety (mainly police and OECD (2015), “Subnational Government Structure and fire protection facilities) are sizable in the United Kingdom, Finance”, OECD Regional Statistics (database), http:// Austria, Switzerland and Germany. dx.doi.org/10.1787/05fb4b56-en. Finally, investment in the social welfare sector (institutions See Annex B for data sources and country-related metadata. for disabled persons, retirement homes for elderly persons, social services centres) represented a small share of SNG investment on average in the OECD but significantly more Reference years and territorial level in Iceland, Norway, Belgium and Denmark. 2014: National Economic Accounts; levels of government. 2013: COFOG data not available for Australia, Canada, Chile, Mexico, New Zealand and Turkey. For the United States, data showed in function “housing and community Definition amenities” include the “environment protection” function General government includes four sub-sectors: data. central/federal government and related public entities; federated government (“states”) and related public entities; local government i.e. regional and Further information local governments and related public entities; and OECD (2016), “Subnational Governments in OECD social security funds. Data are consolidated within Countries: Key data” (brochure), www.oecd.org/gov/ the four sub-sectors. Subnational government is regional-policy/Subnational-governments-in-OECD- defined as the sum of state governments and local/ Countries-Key-Data-2016.pdf. regional governments. OECD (2013), Investing Together: Working Effectively across Capital expenditure is the sum of capital transfers Levels of Government, OECD Publishing, Paris, http:// and investment. Gross fixed capital formation is the dx.doi.org/10.1787/9789264197022-en. main component of investment (see Annex B for a detailed definition). OECD (2015), Recommendation on Effective Public Investment Across Levels of Government – Implementation Toolkit, Investment by economic function follows the www.oecd.org/effective-public-investment-toolkit/. Classification of the ten Functions of Government (COFOG): general public services; defence; public order and safety; economic affairs; environmental Figure notes protection; housing and community amenities; health; recreation, culture and religion; education; 3.11: New Zealand 2013; Turkey 2011. No data for Chile. Per cent change social protection. in real terms. The OECD averages are presented as the weighted 3.12: Other: defence; public order and safety; health; recreation, culture average of the OECD countries for which data are and religion; social protection. Poland, Finland, the Netherlands and the Czech Republic are not represented on the graph because of available, unless otherwise specified (i.e. unweighted negative values in some sectors. average, arithmetic mean, OECD UWA). Data in USD OECD6 and OECD21 refer to federal and unitary countries, use Purchasing Power Parities. respectively. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

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3.12. Breakdown of SNG investment by economic function % of total SNG investment, 2013

Economic affairs General services Education Housing and community amenities Environmental protection Other

GRC

JPN

PRT

IRL

USA

AUT

EST

ESP

OECD6

ISL

OECD27

OECD21

ISR

GBR

EU28

FRA

ITA

DEU

BEL

CHE

NOR

SVN

SVK

LUX

DNK

HUN

SWE

0102030405060708090100 %

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OECD REGIONS AT A GLANCE 2016 © OECD 2016 109 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT Subnational government revenue

In 2014, subnational government (SNG) revenue SNG tax revenue accounted for 7% of GDP in the OECD and represented around USD 6 240 per capita, i.e. 16.0% of gross 32% of public tax revenue in 2014. As above, there are great domestic product (GDP) and 42.3% of public revenue on variations from one country to another (Figure 3.14). Tax-to- average in the OECD. GDP ratio was less than 1% in Estonia, Turkey, the There are two main sources of revenue: taxes (44% of SNG Slovak Republic, Ireland, Greece and Mexico in 2014, but revenue in the OECD on average in 2014) and grants and exceeded 10% in Sweden, Finland, Denmark and three subsidies (38%). It is interesting to note that, on unweighted federal countries (Switzerland, Germany and Canada), average, the proportions are reversed, grants and transfers deriving largely from the personal income tax (a shared tax being the first SNG revenue source (50% vs 34% for taxes). in a number of countries but also a local own-source tax in Finally, revenue deriving from local public service charges Nordic countries for example). Property tax is par excellence a (tariffs and fees) and property (sale and operation of subnational tax, particularly for the municipal level. physical and financial assets) represented respectively However, its importance in SNG tax revenue varies 15% and 2.2% of SNG revenue. The share of tax revenue in considerably across countries, representing between SNG revenue varies a lot from one country to another. They 90% and 100% of local tax revenue in Australia, the are a particularly significant share in some federal United Kingdom, Ireland, Israel and New Zealand, which are countries, where tax revenue arises both from tax sharing mostly Anglo-Saxon countries. At the other end of the arrangements between the federal government and SNGs spectrum, it is a minor local tax revenue source in Nordic (more usually based on personal income tax but also on countries, Luxembourg and Switzerland. company income tax and value added tax) and own-source There are great imbalances in several countries between taxation (Germany, Switzerland, the United States, the level of SNG expenditure as a share of public Canada). However, in Mexico, Austria and Belgium, tax expenditure and the level of SNG tax revenue in public revenue - regardless of whether from tax sharing or own- revenue, reflecting - however imperfectly - the level of sources - provided less than 20% of revenue in 2014. fiscal decentralisation in OECD countries. In unitary countries such as Iceland, Sweden and New Zealand, tax revenue made up more than 50% of local Source revenue, while taxes amounted to less than 15% of local revenue in Estonia, the Netherlands, the Slovak Republic, OECD (2016), National Accounts Statistics (database), http:// Turkey and the United Kingdom. In these countries, as well dx.doi.org/10.1787/na-data-en. as in Austria and Mexico, SNGs depend largely on central OECD (2016), “Subnational Government Structure and government transfers (Figure 3.13). Finance”, OECD Regional Statistics (database), http:// dx.doi.org/10.1787/05fb4b56-en. See Annex B for data sources and country-related metadata. Definition Reference years and territorial level General government includes four sub-sectors: central/federal government and related public National Economic Accounts 2014; levels of government; entities; federated government ("states”) and related Chile, Mexico and New Zealand 2013; Australia 2012; public entities; local government, i.e. regional and Turkey 2011. local governments, and related public entities; and social security funds. Data are consolidated within the four sub-sectors. Subnational government is Further information defined as the sum of state governments and local/ OECD (2016), “Subnational Governments in OECD regional governments. Countries: Key data” (brochure), www.oecd.org/gov/ Revenue comprises tax revenues, transfers (current regional-policy/Subnational-governments-in-OECD- and capital grants and subsidies), tariffs and fees, Countries-Key-Data-2016.pdf. property income and social contributions. Tax revenue includes both own-source tax and shared tax (see Annex B for a detailed definition). Figure notes

The OECD averages are presented as the weighted 3.13: OECD averages do not include Chile. No breakdown available for average of the OECD countries for which data are Chile, except for tax and transfer revenues. available, unless otherwise specified (i.e. unweighted 3.13-3.14: OECD9 and OECD24 refer to federal and unitary countries, average, arithmetic mean, OECD UWA). respectively. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

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Subnational government revenue

3.13. Structure of subnational government revenue, 2014 (%)

Taxes Grants & subsidies Tariffs & fees Property income Social contributions

ISL DEU CHE SWE NZL USA CAN FRA OECD9 JPN ISR CHL FIN ITA OECD33 CZE SVN EU28 ESP PRT OECD24 NOR OECD9 UWA DNK OECD33 UWA OECD24 UWA AUS POL KOR GRC LUX HUN IRL BEL GBR TUR SVK NDL AUT MEX EST 0 102030405060708090100 %

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3.14. Subnational government tax revenue asa%ofpublic tax revenue and as a % of GDP, 2014 Subnational tax revenue as a share of public tax revenue (%) 60 CAN CHE DEU 50 USA

JPN OECD9 40 ESP FIN SWE OECD34

30 CZE ISL DNK KOR EU28 ITA OECD25 POL AUS 20 SVN FRA BEL NOR

HUN ISR 10 MEX CHL PRT NDL SVK NZL IRL GRC AUT GBR EST LUX 0 TUR 0 2 4 6 8 10 12 14 16 Subnational tax revenue as a share of GDP (%)

1 2 http://dx.doi.org/10.1787/888933363717

OECD REGIONS AT A GLANCE 2016 © OECD 2016 111 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT Subnational government debt

The financial and economic crisis led to a strong (“golden rule”), which limits the level of indebtedness. deterioration in both subnational government (SNG) Moreover, local borrowing is generally governed by strict budget balance and debt in most OECD countries. At the prudential rules defined by central or state governments. end of 2014, SNG fiscal balance was about -0.5% of gross Amounting to 71% of total debt on average in the OECD, domestic product (GDP) on average in the OECD. “financial debt” (loans and debt securities) represents the Outstanding gross debt accounted for 23.9% of GDP and largest share of SNG debt (Figure 3.17). Debt securities 19.8% of total public debt (Figure 3.15). represent a large share of SNG debt (45% on weighted SNG outstanding debt is very unevenly distributed among average), especially for states in federal countries (the OECD countries. It is higher in federal countries than in United States, Canada, Germany). Debt securities are also unitary countries: 31% of GDP and 27% of public debt on widespread at the local level in some unitary countries, in average in the first case compared to 15% of GDP and 12% particular Japan, Norway, Korea, Estonia and Sweden. of public debt in the second case. Canada stands out for its However, it is still low or non-existent in numerous unitary high level of subnational debt: 63.5% of GDP and 59% of countries where bond financing is forbidden for local public debt. On the opposite end of the spectrum, SNG debt governments, restricted or rarely used – in comparison to is particularly low in Hungary, Greece, Israel, the loans, which remain the most widespread form of external Slovak Republic and Slovenia, both in terms of GDP and funding (26% of total SNG outstanding debt on weighted weight of public debt. Two unitary countries have high average in the OECD). Other accounts payable (i.e. ratios: Japan (37% of GDP) and Norway (47% of public debt). commercialdebtwithsuppliers)amountedto14%on In federal countries, state government debt varied in 2014 weighted average at the end of 2014. Insurance pensions from 8% of GDP in Austria to around 25% in Spain and (i.e. liabilities related to funded or partially-funded civil Germany and up to 54% in Canada. The local debt varied from servant pension schemes) represent 15% of SNG debt on 5% in Austria to 10% in Switzerland in 2014 (Figure 3.16). weighted average. They are inexistent non-existent (or not recorded) in 23 OECD countries (Figure 3.18). The relatively small share of local government debt in both unitary and federal countries is driven by legal restrictions on local borrowing. In a majority of countries, local Source governments can borrow for the long term only to finance investment in infrastructures and large equipment OECD (2016), National Accounts Statistics (database), http:// dx.doi.org/10.1787/na-data-en. OECD (2016), “Subnational Government Structure and Finance”, OECD Regional Statistics (database), http:// dx.doi.org/10.1787/05fb4b56-en. Definition See Annex B for data sources and country-related metadata. General government includes four sub-sectors: central/federal government and related public Reference years and territorial level entities; federated government (“states”) and related National Economic Accounts; levels of government 2014; public entities; local government, i.e. regional and Iceland, Israel, Japan and Switzerland 2013. No data for local governments, and related public entities; and Chile, Mexico and New Zealand. Non-consolidated debt social security funds. Data are consolidated within data for Japan, Korea, Switzerland and United States; the four sub-sectors. Subnational government is Canada, Japan and Turkey SNA 1993. defined as the sum of state governments and local/ regional governments. Fiscal balance is the difference between government Further information revenues and expenditure. Gross debt includes the OECD (2016), “Subnational Governments in OECD sum of the following liabilities: currency and deposits Countries: Key data” (brochure), www.oecd.org/gov/ + debt securities + loans + insurance pension and regional-policy/Subnational-governments-in-OECD- standardised guarantees + other accounts payable. Countries-Key-Data-2016.pdf. The SNA definition of gross debt differs from the one applied under the Maastricht Protocol (see Annex B for a detailed definition). Figure notes The OECD averages are presented as the weighted 3.15-3.17: OECD9 and OECD25 refer to federal and unitary countries, average of the OECD countries for which data are respectively. available, unless otherwise specified (i.e. unweighted 3.16-3.17: no breakdown available for Australia and United States average, arithmetic mean, OECD UWA). between local and state levels. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

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3.15. Subnational government debt asa%ofGDPandofpublic debt, 2014 Subnational debt as a share of total public debt (%) 70

CAN 60

CHE 50 NOR

40 DEU EST AUS

30 OECD9 ESP SWE USA DNK OECD32 20 FIN EU28 BEL JPN NDL AUT TUR FRA OECD25 10 LUX GBR ISL SVK CZE POL ISR KOR ITA HUN SVN GRC PRT 0 IRL 0 10203040506070 Subnational debt as a share of GDP (%)

1 2 http://dx.doi.org/10.1787/888933363724

3.16. Local and state government debt 3.17. Composition of subnational debt in federal countries % of GDP, 2014 by type of liabilities (%), 2014

Local government Loans Debt securities State government Currency and deposits Local & state government Insurance pension and standardised guarantees Other accounts payable CAN GRC IRL LUX PRT ESP FRA BEL NDL POL OECD8 SVK ESP ISR ITA USA AUT OECD23 NOR FIN DEU JPN SVN ISL EST CHE DNK CZE TUR GBR BEL DEU AUS CHE SWE AUS OECD31 HUN OECD8 KOR AUT CAN USA 0 20406080100 0 20406080 % % 1 2 http://dx.doi.org/10.1787/888933363737 1 2 http://dx.doi.org/10.1787/888933363748

OECD REGIONS AT A GLANCE 2016 © OECD 2016 113 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT Challenges for infrastructure investment at subnational level

The OECD and the EU Committee of the Regions conducted a Almost all SNGs reported gaps in public investment spending. survey in 2015 to assess the challenges linked to Theperceivedfinancinggapsreflectthecompetencies infrastructure investment at the local level across Europe. The allocated to various levels of government. Three-quarters of results of the consultation show that governance challenges SNGs reported having experienced investment funding gaps forinfrastructureinvestmentareprominentatthe for financing roads, and this rate is up to 85% for small subnational level, essentially at the planning stage, and that municipalities (Figure 3.21). Almost half of SNGs reported all levels of government should do more to strengthen the gaps in financing educational infrastructure and 40% have capacities of subnational governments (SNGs) to conduct difficulties in financing infrastructures for economic proper investment strategies. Some of the key findings are development, recreation and culture. summarised below. Challenges for SNG investment go beyond financing and In the EU, 44% of the SNGs surveyed reported a decrease in include different aspects of the investment cycle, from the their investment spending since 2010: 12% by less than planning stage to implementation. 10% and 32% by more than 10% (Figure 3.18). Foregone Three main challenges appear prominent according to the investments concern new investment as well as operations responses to the OECD-CoR survey. and maintenance. These cuts in public investment are more For the vast majority of respondents (90%), the most frequently reported by large SNGs such as regions, inter- important difficulties for infrastructure investment are municipal/regional structures and counties. By contrast, 30% linked to excessive administrative procedures, red tape, of small municipalities (less than 50 000 inhabitants) and and lengthy procurement procedures (Figure 3.22). 28% of medium-sized municipalities have increased their A second type of challenge, more directly connected with the spending by more than 10% since 2010 (Figure 3.19). Smaller responsibility of SNGs, is strategic planning for infrastructure investment projects may be a possible consequence of investment strategies. At the core of planning a lack of co- this trend. ordination across sectors, levels of government and More than half (53%) of the SNGs surveyed reported a jurisdictions is marked as a top challenge by three-quarters decrease in grants from the central government. of SNGs (Figure 3.22). Subnational taxes have proven quite stable in a majority of Finally, lack or weak use of monitoring and results from SNGs since 2010. Furthermore, 39% of SNGs reported a evaluation are recognised as important challenges for at least reduction or stabilization in borrowing to finance 65% of respondents, more prominently by large SNGs investment over the past 5 years and only 12% reported an (regions, large municipalities). In addition, 66% of SNGs increase. Only 4% of SNGs have increased the use of bond consider that a monitoring system exists, but that monitoring financing. This also reflects the fact that bond financing by is pursued as an administrative exercise and not used as a SNGs is not permitted in many EU countries, in particular tool for planning and decision making (Figure 3.22). for municipalities (Figure 3.20). A significant number of SNGs have introduced practices to OftheSNGssurveyed,49%havenoopinionontheprivate improve the governance of infrastructure investment in sector financing of infrastructure (Figure 3.20). This may recent years (Figure 3.23). Improved medium-term reflect a lack of awareness regarding private financing planning for infrastructure investment is seen as key to options. Indeed, 23% have decreased their use of private improving the governance of investment by a majority of sector financing since 2010. Only a minority of cities and SNGs (67%). Increased external support for designing regions (7%) report increasing private sources of financing projects and improved co-operation with neighbouring since 2010, essentially metropolitan areas and regions. Larger local governments to favour economies of scale are equally SNGs may have the extensive technical and legal capacities seen as positive practices, which have helped the required to engage in public private partnerships, while most management of infrastructure investment by two-thirds of SNGs below a certain size do not have those capacities. SNGs surveyed. It should be noted that the simplification of Problematic legal and regulatory environment for public procurement procedures is seen by 20% of the respondents private partnerships is another major challenge, as reported as a practice that has significantly helped the management by 35% of SNGs. of infrastructure investment (Figure 3.23).

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Challenges for infrastructure investment at subnational level

3.18. Change in public investment spending 3.19. Type of SNG with an increase in public investment in the city/region since 2010 spending by more than 10% since 2010

35% Small municipalities < 50 000 inhab. 30

Large municipalities > 50 000 25 inhab.

20 Counties

Inter-municipal co-operation 10 bodies

5 Regions

0 Decrease > Decrease Stable Increase of Increase > Not 0 5 10 15 20 25 30 35 10% of 0-10% 0-10% 10% applicable %

1 2 http://dx.doi.org/10.1787/888933363756 1 2 http://dx.doi.org/10.1787/888933363769

3.20. Change in sources of infrastructure investment 3.21. Sectors most affected by funding gaps funding in the city/region since 2010 in the city/region in the past 5 years

No opinion Decrease Stable Increase Roads

Educational infrastructure Grants from higher levels of government Infrastructures for economic Grants from international development organisations Recreational and cultural infrastructure Local taxes Housing and communities User fees Health infrastructure Property assets Public transport Bank loans Waste and sanitation Multilateral bank loans Administrative buildings

Bonds Water distribution

Private sector financing Energy

0 20406080100 0 20406080 % % 1 2 http://dx.doi.org/10.1787/888933363775 1 2 http://dx.doi.org/10.1787/888933363789

OECD REGIONS AT A GLANCE 2016 © OECD 2016 115 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT

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Source Definition OECD-COR Survey (2015) – Policy highlights Infrastructure The consultation was conducted by the OECD and the planning and investment across levels of government: EU Committee of the Regions between 31 March and Current challenges and possible solutions”, www.oecd.org/ 15 July 2015 in all the official EU languages. effective-public-investment-toolkit/oecd-eu-survey.htm. The survey targeted representatives of subnational governments (regions/ provinces/ landers, counties, municipalities) in charge of investment planning/ Further information financing/monitoring and implementation. OECD (2015), “Recommendation on Effective Public Although the survey does not systematically cover all Investment Across Levels of Government – SNGs in Europe, it provides a picture of challenges Implementation Toolkit (brochure)”, www.oecd.org/ encountered by SNGs. In total, there were effective-public-investment-toolkit/oecd-eu-survey.htm. 296 respondents, 255 of which are SNGs in 27 EU OECD (2013), Investing Together: Working Effectively across Member States (Luxembourg did not participate in Levels of Government, OECD Publishing, Paris, http:// the survey). They represent all categories of SNGs: dx.doi.org/10.1787/9789264197022-en. regions, provinces (25%); intermediary entities (e.g. county, department) (10%); small municipalities i.e. under 50 000 inhabitants (33%); medium municipalities i.e. between 50 000 and 500 000 inhabitants (22%); large municipalities with more than 500 000 inhabitants (2%); and inter-municipal co-operation bodies (8%). 40 additional respondents participated in the survey representing universities, local public enterprises or local agencies with a mixed (public-private) ownership structure.

116 OECD REGIONS AT A GLANCE 2016 © OECD 2016 3. SUBNATIONAL GOVERNMENT FINANCE FOR REGIONAL DEVELOPMENT

Challenges for infrastructure investment at subnational level

3.22. What are the main challenges with respect to strategic planning and implementation of infrastructure investment in your city/ region?

Major challenge Somewhat of a challenge

Excessive administrative procedures and red tape Lenghty procurement procedures Local needs different from priorities at central level Lack of long-term strategy at central level Co-financing requirements for central government/EU are too high Lack of co-ordination across sectors Lack of political will to work across different levels of government Lack of incentive to cooperate across jurisdictions Lack of joint investment strategy with neighbouring SNGs Multiple contact points (absence of a one-stop shop) Lack of (ex-post) impact evaluations Problematic legal/regulatory environment for PPPs Ex-ante analyses not adequately take into account the full project Lack of interest from private partners in conducting PPPs Lack of monitoring in planning Insufficient involvement of civil society in the choice of projects Ex ante analyses not consistently used in decision making Lack of strategic planning capacity Lack of adequate own expertise to design projects No relevant up-to-date data available at local level Disincentive to pursue private investment due to lack of EU funding Increased risks of bribery/corruption 0 102030405060708090100 % 1 2 http://dx.doi.org/10.1787/888933363797

3.23. Which practices have helped the management of infrastructure investment in your city/region?

Significantly Somewhat

Medium-term planning for infrastructure investment External support for designing projects Co-operation with neighbouring local governments Stakeholder engagement at an early stage Simplification of procurement procedures More rigorous selection criteria for investment projects Performance monitoring tools for investment implementation Reduction of administrative/regulatory requirements Information and support from national government More favourable regulatory framework for PPPs/concessions Establishment of a central co-ordination unit 0 20406080100 % 1 2 http://dx.doi.org/10.1787/888933363809

OECD REGIONS AT A GLANCE 2016 © OECD 2016 117

4. INCLUSION AND SUSTAINABILITY IN REGIONS

Concentration of the elderly and children in regions

Demographic challenges of metropolitan areas

Population mobility among regions

Regional disparities in youth unemployment

Part-time employment in regions

Regional access to health

Municipal waste

Household income in metropolitan areas

OECD REGIONS AT A GLANCE 2016 © OECD 2016 119 4. INCLUSION AND SUSTAINABILITY IN REGIONS Concentration of the elderly and children in regions

In all OECD countries, with the exception of Luxembourg, 50 children or more per 100 women in the region with the the elderly population (those aged 65 years and over) has highest value to less than 25 children per 100 women in the dramatically increased over the last decade, both in size region with the lowest value (Figure 4.4). and as a percentage of the total population. Due to higher life expectancy and low fertility rates, the elderly population share has increased, accounting for Definition 16% of the OECD population in 2014. In Japan, Italy, Germany, Greece and Portugal the elderly population was The regional elderly population is the population one-fifth or more of the total population in 2014. The aged 65 years and over. proportion of elderly population is remarkably lower in the The elderly dependency rate is defined as the ratio emerging economies (Brazil, Colombia, Peru and between the elderly population and the working age South Africa), and in Mexico and Turkey (Figure 4.1). (15-64 years) population. The elderly population in OECD countries increased more The child-to-woman ratio is defined as the ratio than five times as much as the rest of the population between the number of children aged 0-4 years and between 2000 and 2014. Significant differences in the growth the number of females aged 15-49. This ratio is of the elderly population share can be found among regions expressed for 100 women. in Canada, Mexico, the United States, Spain and Belgium among the OECD countries, and the Russia Federation and Brazil in the non-OECD countries (Figure 4.2). The elderly dependency rate gives an indication of the Source balance between the retired population and the OECD (2015), OECD Regional Statistics (database), http:// economically active. The elderly dependency rate is dx.doi.org/10.1787/region-data-en. steadily growing in OECD countries. In 2014, this ratio was around 24% in OECD countries, with substantial See Annexes A and B for definitions, data sources and differences between countries (42% in Japan versus 10% in country-related metadata. Mexico). Differences among regions within the same countries were also large. Reference years and territorial level In 2014, the elderly dependency rate across OECD regions was generally higher in rural regions than in urban ones. 2000-14; TL3. This general pattern was more pronounced in certain TL2 regions in Brazil, China, Colombia, Peru, countries such as Japan, the Netherlands, Portugal, Spain, Russian Federation and South Africa. the United Kingdom, Australia and Korea (Figure 4.3). On the other hand, in Hungary, Poland and the Slovak Republic Further information the elderly dependency rate was on average higher in predominantly urban regions than in rural regions. The Territorial grids and regional typology (Annex A). higher the regional elderly dependency rate, the greater the challenges faced by regions in generating wealth and sufficient resources to provide for the needs of the Figure notes

population. Concerns may arise on the financial self- 4.1-4.4: Latest available year: Mexico 2010. First available year: Australia sufficiency of these regions to generate taxes to pay for and Japan 2001, South Africa 2002, Brazil 2004. these services. 4.3: In order to better show the disparities between rural and urban The child-to-woman ratio is a measure of fertility, and at regions, intermediate regions are not represented in this figure. No regional level it may also reveal specific needs in health rural regions in the Netherlands and New Zealand. and personal services. In Turkey, Canada, Mexico and Chile 4.4: Israel and Turkey 2013. the children-to-woman ratio ranges across regions from Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

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Concentration of the elderly and children in regions

4.1. Elderly population as a % of the total population, 4.2. Yearly growth of regional elderly population, 2000 and 2014 2000-14 (TL3)

2014 2000 Minimum Country average Maximum

JPN CAN ITA DEU MEX GRC USA PRT ESP FIN BEL SWE KOR EST AUT AUS DNK GBR FRA NLD ESP POL BEL CHE IRL HUN NOR SVN CHE GBR JPN CZE DEU NLD OECD34 ITA NOR GRC CAN PRT POL AUT AUS USA FRA NZL CZE LUX ISL SVK NZL ISL FIN KOR IRL SWE ISR SVN CHL HUN TUR EST MEX Total42 SVK LVA LTU RUS RUS BRA CHN COL BRA COL PER PER ZAF ZAF LTU LVA 0 5 10 15 20 25 30 % - 4- 2 0 2 4 6 8 10 % 1 2 http://dx.doi.org/10.1787/888933363812 1 2 http://dx.doi.org/10.1787/888933363826

4.3. Elderly dependency rate for countries, 4.4. Regional variation in child-to-woman ratio, predominantly urban and predominantly children per 100 women, rural regions, 2014 (TL3) 2014 (TL3)

Predominantly urban Country Predominantly rural Minimum Country average Maximum JPN PRT TUR NLD CAN SWE MEX GRC CHL ISR ESP ESP FRA USA ITA FRA GBR GBR NZL DEU AUS FIN JPN DNK DEU FIN EST BEL LTU NLD LVA PRT NOR POL GRC AUS ITA AUT KOR CAN NOR SVK SVN DNK BEL SVN CZE ISL KOR HUN CHE HUN AUT CHE IRL USA CZE EST ISL SWE POL IRL COL SVK RUS PER CHL BRA TUR ZAF MEX LTU NZL LVA 0 10 20 30 40 50 60 0 10 20 30 40 50 60 70 % 1 2 http://dx.doi.org/10.1787/888933363832 1 2 http://dx.doi.org/10.1787/888933363847

OECD REGIONS AT A GLANCE 2016 © OECD 2016 121 4. INCLUSION AND SUSTAINABILITY IN REGIONS Demographic challenges of metropolitan areas

Metropolitan areas are generally the destination of young Elderly dependency rate can be quite different across migrants. Despite this, population ageing has become also metropolitan areas in the same country. For example, the an urban phenomenon in many OECD countries. The difference between Genoa and Naples in Italy, and between elderly dependency rate, i.e. the ratio between the elderly Naha and Shizouka in Japan is more than 22 percentage population and the working age population, across the points (Figure 4.7). 281 OECD metropolitan areas was equal to 22% in 2014, very close to the 24% average in OECD countries. However, while in Japanese metropolitan areas the elderly dependency rate was on average 40% in 2014, in Mexico Source metropolitan areas this was below 10% (Figure 4.5). Over the period 2000-14, OECD metropolitan areas have OECD (2015), “Metropolitan areas”, OECD Regional Statistics experienced a general rise in the elderly dependency rate (database), http://dx.doi.org/10.1787/data-00531-en. (on average, a 4 percentage point increase). The elderly dependency rate increased by more than 5 percentage points on average in the metropolitan areas of Japan, Reference years and territorial level Germany, Italy, Portugal and Greece, between 2000 and 2014. Over the same period, the elderly dependency 2000-14; metropolitan areas. rate decreased in the metropolitan areas of Belgium, The FUA have not been identified in Iceland, Israel, Norway and the United Kingdom (Figure 4.5). New Zealand and Turkey. The FUA of Luxembourg does not The increase in the elderly dependency rate is due to the appear in the figures since it has a population below rapid increase of the elderly population in all metropolitan 500 000 inhabitants. areas (on average 2.8% annual growth rate in the period 2000-14) and a moderate increase in the working age population (on average 1% annual growth rate over the Further information same period). In the metropolitan areas of Estonia, OECD (2015), Ageing in Cities, OECD Publishing, Paris, http:// Germany, Japan and Greece the working age population dx.doi.org/10.1787/9789264231160-en. declined over the period under analysis (Figure 4.6). OECD (2014), Society at a Glance 2014: OECD Social Indicators, OECD Publishing, Paris, http://dx.doi.org/10.1787/ soc_glance-2014-en. Definition OECD (2012), Redefining “Urban”: A New Way to Measure 281 Metropolitan areas have been identified for Metropolitan Areas, OECD Publishing, Paris, http:// 30 OECD countries. They are defined as the functional dx.doi.org/10.1787/9789264174108-en. urban areas (FUA) with a population above 500 000. Functional urban areas can extend across administrative boundaries, reflecting the economic Figure notes geography of where people actually live and work. 4.5-4.7: Country metropolitan average refers to the average of all The elderly dependency rate is defined as the ratio metropolitan areas in a country. Metropolitan population figures are between the elderly population and the working age estimates based on municipal figures for the last two census (15-64 years) population. available for each country. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

122 OECD REGIONS AT A GLANCE 2016 © OECD 2016 4. INCLUSION AND SUSTAINABILITY IN REGIONS

Demographic challenges of metropolitan areas

4.5. Elderly dependency rate 4.6. Annual average growth rates in OECD in OECD metropolitan areas, metropolitan areas (working age population 2000 and 2014 and elderly population), 2000-14

Metropolitan areas (2000) National (2000) 15-64 65+ Metropolitan areas (2014) National (2014) Country (No. of cities) Country (No. of cities) JPN (36) MEX (33) DEU (24) KOR (10) ITA (11) CHL (3) PRT (2) JPN (36) GRC (2) IRL (1) AUT (3) FIN (1) CHE (3) OECD (281) HUN (1) AUS (6) SVN (1) SVN (1) EST (1) PRT (2) DNK (1) FRA (15) ESP (8) CAN (9) SWE (3) CHE (3) CZE (3) DEU (24) BEL (4) AUT (3) FRA (15) ESP (8) FIN (1) ITA (11) NLD (5) NLD (5) OECD29 EST (1) GRC (2) POL (8) USA (70) GBR (15) DNK (1) AUS (6) CZE (3) NOR (1) POL (8) CAN (9) SWE (3) SVK (1) NOR (1) USA (70) SVK (1) IRL (1) HUN (1) CHL (3) GBR (15) KOR (10) BEL (4) MEX (33) -1012345 01020304050 % % 1 2 http://dx.doi.org/10.1787/888933363852 1 2 http://dx.doi.org/10.1787/888933363861

4.7. Countries ranked by differences in old age dependency ratio at metropolitan level, 2014

Minimum Country metropolitan average Maximum % 60 Shizuoka

50 Genova Dresden Toulon 40 Bilbao Basel Clearwater/St. Petersburg Lisbon Eindhoven Portsmouth Antwerp Ostrava Athens 30 Vienna Budapest Malmö Ljubljana Tallinn Copenhagen Adelaide Łód Quebec Helsinki Oslo Naha Bratislava Busan 20 Valparaíso Graz Dublin Porto Münster Veracruz Naples Paris Seville Geneva Prague Ghent Thessalonica Stockholm

10 Utrecht London Pozna Brisbane Austin Calgary Ulsan Benito Juárez Benito 0 Concepción

Country (No. of cities)

1 2 http://dx.doi.org/10.1787/888933363874

OECD REGIONS AT A GLANCE 2016 © OECD 2016 123 4. INCLUSION AND SUSTAINABILITY IN REGIONS Population mobility among regions

Inter-regional mobility within countries is an important component of the change in the demographic structure Definition and in the labour force supply. Data refer to yearly flows of population from one In the 29 observed OECD countries, 24 million people TL3 region to another TL3 region of the same country changed their region of residence yearly in the period (regional migration). Outflows are represented as the 2011-13. This movement corresponded to almost 5% of number of persons who left the region the previous total population in Korea and Hungary, less than 0.5% in year to reside in another region of the country, while the Slovak Republic and 2% of the total population in the inflows are represented as the number of new OECD area, more than four times the value of the residents in the region coming from another region of international migration rate to OECD countries (Figure 4.8). the country. Regional migration does not affect all regions of a country The net migration flow is defined as the difference equally: Division 16, Alberta (Canada), Gümüshane (Turkey) between inflows and outflows in a region. A negative and North Aegean (Greece) were the TL3 regions with the net migration flow means that more migrants left the highest positive net migration rate, 3.4%, 2.2% and 1.6% region than entered it. of the regional population, respectively. Agri (Turkey), Young migrants are those aged between 15 and 29. the Northern Territory-Outback (Australia) and the Region 5, Northwest Territories (Canada) were among the TL3 regions with the highest negative net migration rates (Figure 4.9). On aggregate, the net migration rate in the predominantly urban regions of 26 OECD countries was 6 people per Source 10 000 population in 2011-13 versus -2 and -10 in intermediate and rural regions, respectively. However, net OECD (2015), OECD Regional Statistics (database), http:// migration rates were negative in urban regions in Korea, dx.doi.org/10.1787/region-data-en. the United Kingdom, Switzerland, Australia, Portugal and See Annex B for data sources and country-related metadata. Belgium. On average rural regions were net recipients of regional migration in Korea, Belgium, Switzerland, the United Kingdom and Portugal (Figure 4.10). Reference years and territorial level Distance to labour markets and services seems to explain migration within OECD countries: with the exception of 2009-13; TL3. Turkey,theUnitedStates,andSweden,remoterural Data for Chile, France, Ireland and New Zealand are not regions – i.e. regions which are far in driving distance from available at regional level. urban agglomerations – show higher net negative flows than predominantly rural regions. Further information The mobility of youth aged from 15 to 29 years old, which represents 10% of the total internal mobility for the observed Territorial grids and regional typology (Annex A). 17 countries, is, on average, a migration from rural to urban regions where higher education facilities and more diverse job opportunities can be found. In Korea, Germany, the Figure notes United Kingdom, Japan and the Slovak Republic, more than 4.8-4.10: Available years: Germany 2011-12; Greece, Portugal, Slovenia 90% of young migrants move to predominantly urban and United States 2011; Mexico and Netherlands 2010. regions. Rural regions in Japan will bear the largest share of 4.9: For Canada, the migration flows exclude the region of Stikine, the future decline in population because of the already high British Columbia. incidence of an elderly population reinforced by out- 4.11: Available years: Germany 2009-12, Japan 2010-13, Portugal 2011. migration of young people. In contrast, the youth migration United Kingdom data do not include Scotland and Northern Ireland. flows towards Izmir (Turkey), Gyeonggi-do (Korea) or Inner Greece and Iceland do not have net positive flows in predominantly London-West (United Kingdom), even if still positive, urban regions. decreased by 30% in the years following the economic crisis Information on data for Israel: http://dx.doi.org/10.1787/888932315602. (Figure 4.11).

124 OECD REGIONS AT A GLANCE 2016 © OECD 2016 4. INCLUSION AND SUSTAINABILITY IN REGIONS

Population mobility among regions

4.8. Annual regional migration rate, 4.9. Maximum and minimum annual regional average 2011-13 migration rate, average 2011-13 Flows across TL3 regions, % of total population Net flows across TL3 regions, % of total population

KOR Minimum Maximum HUN GBR NLD TUR Agri Gümüshane AUS RUS Magadan Oblast Moscow Oblast AUS Northern Territory - Outback Bunbury TUR CAN Northwest Territories Alberta MEX KOR Seoul Jeju-do ISR ESP Cuenca La Palma DNK USA Austin-Round Rock, TX Wichita Falls, TX NOR GRC Attica North Aegean ISL GBR Inner London - West East Sussex CC DEU BEL Brussels-Capital Walloon Brabant ISL Northwestern Capital Region FIN DEU Göttingen Westsachsen SVN ISR Zefat Sub-District Ramla Sub-District OECD29 CHE Basel-Stadt Fribourg SWE POL City of Poznan Poznanski JPN DNK Bornholm City of Copenhagen AUT NOR Finnmark Akershus CHE EST Northeast Estonia North Estonia GRC AUT Traunviertel Wiener Umland/Nordteil HUN Nógrád Gyor-Moson-Sopron BEL CZE Karlovy Vary Central Bohemia USA JPN Fukushima Tokyo ESP FIN Kainuu Pirkanmaa PRT BRA Distrito Federal Piauí EST ITA Vibo Valentia Bologna CZE SWE Jämtland Halland County CAN SVK Prešov Region Bratislava PRT Alto Alentejo Oeste POL SVN Central Sava Coastal-Karst ITA NLD Southeast Friesland Gooi and Vecht SVK -4 -3 -2 -1 0 1 2 3 0123456 % % 1 2 http://dx.doi.org/10.1787/888933363889 1 2 http://dx.doi.org/10.1787/888933363899

4.10. Annual regional migration rate per typology 4.11. Young migrants in urban regions as a % of region, average 2011-13 of young migrants in the country, 2009 and 2013 Net flows across TL3 regions per 10 000 population Positive net flows of youth migration across TL3 regions

Rural Intermediate Urban 2013 2009

SVK KOR EST DNK DEU HUN GBR TUR CZE JPN SWE SVK FIN NOR DNK AUT POL HUN JPN OECD15 ESP DEU CHE OECD25 CZE USA ITA NOR NLD FIN BEL PRT TUR AUS SWE CHE GBR POL KOR SVN PRT ISL ITA -80 -60 -40 -20 0 20 40 60 80 0 20406080100 % 1 2 http://dx.doi.org/10.1787/888933363901 1 2 http://dx.doi.org/10.1787/888933363916

OECD REGIONS AT A GLANCE 2016 © OECD 2016 125 4. INCLUSION AND SUSTAINABILITY IN REGIONS Regional disparities in youth unemployment

The long-term development of societies, politically and exclusion of the youth in the productive side of the economy, economically, depends to a great extent on the knowledge, but also the fact that they are not acquiring the skills and skills, values and competences acquired by people at an competences necessary for both their long-term individual early age. Educational and working opportunities for the well-being and the long-term development of their country. young people are also fundamental to enhance social Strikingly, the regions of Southeastern Anatolia-East cohesion, by discouraging people from engaging in illegal (Turkey), Central Greece and Sicily (Italy) have a NEET rate of activities, reducing political and social conflict, and 52.3%, 43.4% and 42.1% respectively; on the other hand, the increasing trust in others and in institutions. regions of Tel Aviv District (Israel), Hokuriku (Japan), Western In the lagging regions of seven OECD countries and Norway and Southwest Overijssel (Netherlands) have NEET three non-OECD countries, youth unemployment has rates below the 5% (Figure 4.14). decreased since 2008. Among the OECD countries, Germany, Chile, France and Israel experienced the largest decreases in youth unemployment over the period 2008-14 Source (Figure 4.12). OECD (2015), OECD Regional Statistics (database), http:// Important regional disparities in youth unemployment still dx.doi.org/10.1787/region-data-en. remain within OECD countries; Italy, Greece, Turkey and See Annex B for data sources and country-related metadata. Spain present the largest subnational gaps in this indicator, 47, 43, 27 and 24 percentage point regional differences, respectively (Figure 4.13). Reference years and territorial level Another important indicator that depicts a lack of opportunities for the youth in a broader sense is the rate of Youth unemployment and NEET: 2014 or latest available; young people neither in employment nor in education and TL2 except for New Zealand for which data is available only training (NEET). This indicator is particularly important for the regions of North Island and South Island. since it not only reveals, to a certain extent, the current Further information

OECD (2015), How's Life? 2015: Measuring Well-being,OECD Definition Publishing, Paris, http://dx.doi.org/10.1787/how_life-2015-en. The youth unemployment rate is defined as the ratio between unemployed persons aged between 15 and 24 Figure notes and the labour force in the same age class (expressed as a percentage). 4.12-4.13: Korea is not included. Reference years: Greece, The indicator rate of young people neither in Slovak Republic and Switzerland 2009; Chile, France and Mexico 2010; Portugal 2012. Latest available years: Iceland 2011; employment nor in education and training (NEET) New Zealand 2012; and Brazil and Israel 2013. corresponds to the percentage of the population aged 4.12: The change from the reference year to the latest year available 18-24 that is neither employed nor involved in further corresponds to the change in percentage points of the average youth education or training with respect to the population unemployment of the regions with the highest unemployment rate of the same age class. Regional comparable values are and with the 20% of the country’s youth (15-24 years old) population. available only for Europe. The regions of Burgenland, Carinthia, Salzburg, Vorarlberg (Austria); Åland (Finland); Corsica (France); Zeeland (New Zealand); and Lagging regions are here defined as the regions with the Bremen and Saarland (Germany) are not included. highest unemployment rate and that concentrate 20% 4.14: Australia, Canada, Chile, Israel, Korea and Mexico are not included. of the country’s youth (15-24 years old) population. Latest available years: Brazil, Israel and South Africa 2013. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

126 OECD REGIONS AT A GLANCE 2016 © OECD 2016 4. INCLUSION AND SUSTAINABILITY IN REGIONS

Regional disparities in youth unemployment

4.12. Change in the youth unemployment rate 4.13. Regional variation between 2008 and 2014, country average in youth unemployment rate, and lagging regions, (percentage points) (TL2) 2014 (TL2)

Lagging regions Country average Minimum Country average Maximum

PRT ITA DEU GRC CHL TUR FRA ESP ISR BEL HUN JPN POL MEX PRT NOR SVK TUR HUN SVK CHL USA USA CHE CZE GBR MEX CAN NLD SWE AUT FIN FRA EST CHE AUT DEU CZE CAN AUS LUX GBR DNK SVN ISL AUS BEL SWE NZL IRL OECD33 ISR NLD JPN POL FIN SVN NZL IRL NOR ITA DNK GRC ISL ESP LUX EST COL RUS BRA RUS LTU BRA LVA COL LVA -10 0 10 20 30 LTU % 0 20 40 60 80 % 1 2 http://dx.doi.org/10.1787/888933363925 1 2 http://dx.doi.org/10.1787/888933363930

4.14. Regional variation in the rate of young people NEET, 2014 (TL2)

Minimum Country average Maximum % 60

50 Azores Ceuta Chocó

40 Republic Chechen Amapá KwaZulu-Natal Central Greece Central Podkarpacia

30 Wales Southeastern Anatolia - Anatolia Southeastern East Picardy Brussels Capital Region Capital Brussels Northwest Ticino Eastern Slovenia Eastern Northern Hungary Northern Central Slovakia Central Northern Territory 20 Region Manawatu-Wanganui Zeeland Flanders Zeeland Northern Jutland Northern Berlin Border, Midland and Western Midland Border, Shikoku Northern District Northern Vienna Luxembourg North Middle Sweden Middle North Estonia Southern Finland Southern 10 Norway Northern Central Portugal Central Basque Country Basque Attica Thrace Mazovia 0 Brittany Western Cape Western Prague Greater London Greater Tyrol Capital (DK) Capital Bavaria Hokuriku Santa Catarina Santa Flemish Region Flemish

- 10 Slovenia Western Tel Aviv District Bogotá Capital District Capital Bogotá Otago Region Otago Northwestern Switzerland Northwestern Southwest Overijssel Southwest Capital Territory City of Moscow Bratislava Region Bratislava Western Norway Western Helsinki-Uusimaa Central Transdanubia Central Småland with with Småland Islands - 20 and Southern Eastern

1 2 http://dx.doi.org/10.1787/888933363941

OECD REGIONS AT A GLANCE 2016 © OECD 2016 127 4. INCLUSION AND SUSTAINABILITY IN REGIONS Part-time employment in regions

Part-time employment has increased in many OECD and Chile where, in 2014, the region with highest share of countries during the past years, representing almost one- part-time employment has a value more than twice as high fifth of total employment in 2014. Depending on the as the region with the lowest value (Figure 4.15). institutional and economic context, part-time employment The gender composition of part-time employment is can have opposing effects on the well-being of the working influenced not only by regional demographic population. On the one hand, part-time workers may suffer characteristics but also by regulatory settings and access to a penalty compared to their full-time counterparts in terms family-oriented services (for example child and elderly care of job-security, training, promotion, and unemployment services), which may contribute to increasing the benefits. On the other hand, part-time employment can participation of women into the workforce. In the Province offer a better family-friendly working-time arrangement. In of Bolzano-Bozen (Italy), Vorarlberg (Austria), Bavaria general, in the presence of the right incentives, part-time (Germany), Basque Country (Spain), Franche-Comté jobs seem to promote labour force participation and can be (France), Northern Aegean (Turkey) and Wallonia (Belgium), a relevant alternative to inactivity (OECD, 2015a). women account for more than 80% of the total part-time The incidence of part-time employment is not evenly employment, 10 percentage points higher than the OECD distributed across OECD regions. Regions in the average (Figure 4.16). Regions with small shares of women Netherlands and Switzerland show the highest shares of working part-time are Southeastern Anatolia-East (Turkey), part-time employment across the OECD countries Alentejo (Portugal), West Greece and East Slovakia, where considered; while the regions with the lowest share of part- the share of women in part-time employment is lower time employment are found in Eastern European countries than 50% of the total part-time employment (Figure 4.16). such as the Slovak Republic, Hungary, Estonia, the Czech Republic and Poland (Figure 4.15). Large regional disparities within countries are found in Turkey, Australia Source OECD (2015), OECD Regional Statistics (database), http:// dx.doi.org/10.1787/region-data-en.

Reference years and territorial level Definition The definition of part-time work varies considerably 2014; TL2. across OECD member countries. The OECD defines Israel, Norway, United States and Brazil 2013. part-time working in terms of usual working hours New Zealand 2012. fewer than 30 per week. However, for European No regional data are available for Iceland and Korea. TL2 regions, the distinction between full-time and part-time work is based on a spontaneous response by the respondent; except in the Netherlands, Iceland Further information and Norway, where part-time is determined if the usual hours are fewer than 35 hours. OECD (2015), “The incidence of part-time employment has continued to increase: Percentage of employees aged At regional level, a harmonised definition of part-time 15-64, 2007-14”, in OECD Employment Outlook 2015, OECD employment does not exist. Indeed, for some Publishing, Paris, http://dx.doi.org/10.1787/empl_outlook- countries, the number of hours defining the number of 2015-graph7-en. part-time employees in a region differs from the OECD definition. This makes regional values differ from national estimates relying on a harmonised definition. Figure notes Incidence of part-time employment refers to the 4.16: Female part-time employment data exclude the regions of Yukon, proportion of part-time employees with respect to the Northwest Territories and Nunavut for Canada, and Åland for total number of employed persons in a region. Finland. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

128 OECD REGIONS AT A GLANCE 2016 © OECD 2016 4. INCLUSION AND SUSTAINABILITY IN REGIONS

Part-time employment in regions

4.15. Regional variation in the percentage of part-time employment, 2014 (TL2)

Minimum Country average Maximum

% East Groningen 60

50 Espace Mittelland Espace

40 Tasmania NE Anatolia - NE Anatolia East Bremen Bremen Flevoland Hedmark and Oppland Hedmark Cajamarca South West West England South Araucanía Jerusalem District Vienna Småland with with Småland Islands South Island South Republic of of Dagestan Republic 30 Capital Border, Midland and Western Midland Border, Flemish Region Flemish Toukai Bolzano-Bozen Hidalgo Valencia Corsica British Columbia Maine Åland Ticino 20 Madeira Thessaly Alagoas Western Slovenia Western Podkarpacia Prague Southern Transdanubia Southern Zealand Cundinamarca Central Slovakia Central

10 Stockholm Burgenland Brandenburg Tohoku North Island North Greater London Greater Central District Campania Ceuta Ceuta Oslo and Akershus and Oslo

0 Antofagasta Alentejo Ile-de-France Northern Territory Madre de Madre dios Southern and Southern Eastern São Paulo Queretaro Cesar Northwest Helsinki-Uusimaa Brussels Capital Region Capital Brussels West Slov. West NW Territories Eastern Slovenia Eastern Warmian-Masuria Nenets DistrictColumbia of North Aegean North Northern Aegean Northern Yamalo- - 10 Trans. Central

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4.16. Share of female part-time employment: Highest and lowest, 2014 (TL2)

Minimum Country average Maximum (region name) % 100

90 Vorarlberg Federal City of City Federal Bolzano-Bozen Saint Petersburg Saint Bavaria Basque Country Basque Franche-Comté Northern Aegean Northern Wallonia Cesar Moravia-Silesia Northern Ireland Northern

80 Switzerland Eastern Goiás Zeeland Flanders Zeeland South Island South Colima North Middle Sweden Middle North West Slovakia West Toukai Western Australia Western Alberta Central District Agder and Agder Rogaland West Pomerania West Lambayeque West Pomerania West West Macedonia West 70 Denmark Southern Arica Y Parinacota Y Arica Mississippi Central Hungary Central Southern Finland Southern Lisbon Western Slovenia Western 60

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Health services and doctors are distributed unequally lagging regions of Israel, Portugal, Finland, Estonia, Norway across different regions in most OECD countries, and this and Slovenia (an average increase of 0.5 physicians per causes concern about how to ensure access to health 1 000 inhabitants); whereas the lagging regions of Spain and everywhere and foster better health outcomes. Belgium have worsened over the same period (an average The most important regional differences in the number of decrease of 0.7 doctors per 1 000 inhabitants) (Figure 4.19). hospital beds per 10 000 inhabitants can be found in Japan, Poland and Germany. Very low levels of hospital beds are Source found in Nuevo Leon (Mexico), Tarapaca (Chile), Yukon (Canada) and Southeastern Anatolia-East (Turkey), OECD (2015), OECD Regional Statistics (database), http:// where the hospital beds were below 20 per every 10 000 dx.doi.org/10.1787/region-data-en. inhabitants in 2013 (Figure 4.17). See Annex B for data sources and country-related metadata. In 2013, the largest regional disparities in the density of physicians were found in the United States, Greece, the Czech Republic and the Slovak Republic. While regions Reference years and territorial level such as the District of Columbia (United States) and Attica Density of physicians: 2013 or latest available; TL2 except (Greece) had levels close to 9 active physicians per every for Estonia which is presented at the TL3 level, and for 1 000 inhabitants, in Illinois (United States) and Central New Zealand for which data is available only for the Greece the density of physicians was below 3 doctors per regions of North Island and South Island. 1 000 people (Figure 4.18). Hospital bed rate: 2013 or latest available; TL2 except for In the period 2008-13, the change in the density of Estonia which is presented at the TL3 level. physicians has been modest in the lagging regions, i.e. those regions with the lowest density of physicians and concentrating 20% of the country’s population; on average it Further information increased only by 0.2 doctors per 1 000 inhabitants. OECD (2015), How’s Life? 2015: Measuring Well-being,OECD Nevertheless, some improvements can be observed in the Publishing, Paris, http://dx.doi.org/10.1787/how_life-2015-en. Ono,T., M. Schoenstein and J. Buchan (2014), “Geographic Imbalances in Doctor Supply and Policy Responses”, OECD Health Working Papers,No.69,OECDPublishing, Paris, http://dx.doi.org/10.1787/5jz5sq5ls1wl-en. Definition The number of physicians includes general Figure notes practitioners and specialists actively practicing medicine during the year in both public and private 4.17: Iceland, Korea, New Zealand and United Kingdom are not included. institutions. Density of physicians is defined as the Latest available years: Netherlands 2002; Chile and United States 2009; Belgium, Canada, Japan and Luxembourg 2010; number of active physicians per every 1 000 people. Greece and Mexico 2011; Australia, Israel, Italy and Sweden 2012. The number of hospital beds refers to beds in all 4.18-4.19: Iceland and Ireland are not included. Reference years: hospitals, including general hospitals, mental health Peru 2007; Chile 2010. Latest available years: New Zealand and and substance abuse hospitals, and other specialty United Kingdom 2010; Canada, Chile, Luxembourg and hospitals. Hospital bed rate is defined as the number United States 2011; Australia, Belgium, Denmark, Israel, Japan, Peru of hospital beds for every 10 000 people. and Sweden 2012; and Korea 2014. 4.19: The change from the reference year to the latest year available Lagging regions are here defined as the regions with corresponds to the change in the density of physicians (active the lowest density of physicians and that concentrate physicians per 1 000 inhabitants) of the regions with the lowest 20% of the country’s population. density of physicians and with 20% of the country’s population. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

Regional access to health

4.17. Regional variation in hospital bed rate 4.18. Regional variation in density of physicians (per 10 000 inhabitants), 2013 (TL2) (per 1 000 inhabitants), 2013 (TL2)

Minimum Country average Maximum Minimum Country average Maximum

JPN USA POL GRC CZE DEU SVK PRT NLD NLD PRT GRC AUT USA ESP EST AUT MEX CAN DEU EST FIN SVK ISR TUR FRA HUN CZE CHE TUR ITA ISR DNK FIN FRA NOR CHE SWE BEL AUS ESP BEL NOR GBR POL SVN JPN CHL SVN ITA CHL HUN CAN SWE KOR LUX AUS NZL DNK MEX RUS IRL PER CHN LUX 0 2 4 6 8 10 0 50 100 150 200 1 2 http://dx.doi.org/10.1787/888933363978 1 2 http://dx.doi.org/10.1787/888933363986

4.19. Change in density of physicians (per 1 000 inhabitants) between 2008 and 2013, (TL2)

Lagging regions Country average

% 0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

-0.6

-0.8

-1.0

-1.2

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Municipal waste management and treatment play an Similarly, large differences are observed at the regional important role to abate and control pollution. Indeed, it level in terms of recycled waste. The largest differences, helps prevent the formation of greenhouse gas emissions, among the 18 OECD countries where data are available, are such as methane and other toxic gases, which form observed in the Slovak Republic, Germany and Poland through the degradation of organic waste in landfills, (Figure 4.22). particularly in warmer climates. Additionally, efficient waste management reduces the risk of spreading diseases. Source Waste production depends largely on consumption, production patterns, lifestyles, among other things. In National data: OECD (2016), OECD Environmental Statistics 2013, 518 kg per capita of municipal waste were produced (database), http://dx.doi.org/10.1787/env-data-en. on average across OECD countries. This figure varied from Regional data: OECD (2016), OECD Regional Statistics 293 kg per capita in Estonia to 751 kg per capita in Denmark (database), http://dx.doi.org/10.1787/region-data-en. (Figure 4.20). Over the past 20 years, municipal waste generated in the OECD area was stable. The largest OECD (2015), “Metropolitan areas”, OECD Regional Statistics increases were experienced in countries such as Denmark, (database), http://dx.doi.org/10.1787/data-00531-en. Greece, and Austria (over 140 kg per capita in this period), while countries such as New Zealand or Slovenia registered Reference years and territorial level the largest decrease in the municipal waste production per capita (more than 180 kg per capita). Despite the use of 2013; TL2. different methodologies in accounting for national waste No regional data are available for Australia, Switzerland, which could influence the comparison of national data, Denmark, Finland, Greece, Iceland, New Zealand, Sweden 51% of the countries under analysis show improvements in and the United States. See Annex B for data sources and waste management practices over this period. country-related metadata. The sum of collected regional data Municipal waste differences also exist within the same on waste does not always match the OECD national data. country (Figure 4.21). Significant regional differences No municipal waste recycling data at regional level are between the lowest and the highest regions in terms of available for Australia, Canada, Chile, Denmark, Finland, waste per capita exist in Chile, Spain, Mexico and Canada. Greece, Iceland, Ireland, Israel, Mexico, New Zealand, Spain, Sweden, Switzerland, Turkey and the United States.

Further information Definition OECD (2015), Environment at a Glance 2015: OECD Indicators, Municipal waste is generally defined as the total OECD Publishing, Paris, http://dx.doi.org/10.1787/ waste collected by or on behalf of municipalities. It 9789264235199-en. includes waste from households, commerce, institutions and small businesses, yard and garden. The definition excludes municipal waste from Figure notes construction and demolition and municipal sewage. 4.20: Latest available year: Australia and Chile 2009; Japan 2010; Austria, Waste recycled refers to the total waste recycled or Greece, Ireland, Korea, Mexico, United States 2012. First available incinerated (including composting). year: Australia and Israel 2000. Information on data for Israel: http://dx.doi.org/10.1787/888932315602.

Municipal waste

4.20. Municipal waste (kg per capita), 1995 and 2013

2013 1995 Kg per capita 900

800

700

600

500

400

300

200

100

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4.21. Range in regional municipal waste per capita, 4.22. Range in regional municipal waste recycled 2013, (TL2) country average value = 100 per capita, 2013, (TL2) country average value = 100

Minimum Maximum Minimum Maximum

CHL SVK ESP DEU MEX CAN POL KOR CZE PRT HUN PRT POL HUN GBR ITA ITA TUR FRA SVK GBR ISR FRA KOR AUT JPN NLD AUT CZE NOR NOR BEL NLD SVN BEL JPN IRL SVN LUX LUX EST EST 0 50 100 150 200 250 300 350 400 % 0 100 200 300 400 % 1 2 http://dx.doi.org/10.1787/888933364017 1 2 http://dx.doi.org/10.1787/888933364027

OECD REGIONS AT A GLANCE 2016 © OECD 2016 133 4. INCLUSION AND SUSTAINABILITY IN REGIONS Household income in metropolitan areas

People living in metropolitan areas have higher income Larger metropolitan areas have on average higher income, than those living outside metropolitan areas. According to but also higher inequalities. Larger cities often concentrate the estimation of household disposable income in 18 OECD the most skilled people with high-income and this is countries, based in most cases on tax records, the average reflected in the observed inequality. Gini coefficients for income in metropolitan areas is on average 17% higher household disposable income were estimated for 121 than elsewhere (Boulant et al., 2016). The income premium metropolitan areas in 11 OECD countries. According to in metropolitan areas with respect to the national average these estimations, large differences within countries can is always positive, with the exceptions of Belgium, but it be observed in the income inequality of the different can differ significantly across countries (Figure 4.23). metropolitan areas. For example, in the United States, the Mexico is the country where the difference between metropolitan areas of Albany, NY and Columbia, SC had a income of metropolitan and non-metropolitan residents is Gini coefficient for household disposable income close to the highest (68%), followed by Hungary (37%), Estonia (34%) 0.30, which was much lower than those encountered in and Chile (23%). It should be acknowledged, however, that Miami, FL and in McAllen, TX (close to 0.43), in 2014. relatively higher incomes do not necessarily imply a higher Similarly, income inequalities in Brussels was 0.38 in 2013, purchasing power available to metropolitan residents. In much higher than in Antwerp, Liège and Gent, where the fact, differences in living costs between locations can offset Gini coefficient ranged between 0.29 and 0.31. Mexico is partially earning differences across urban and rural places. another country showing large differences in income inequality across its largest cities. Differently from Belgium, however, the capital city, which is also the largest metropolitan area, does not record the highest levels of Definition income inequality. The Gini coefficient in Reynosa was 0.41 Thedisposableincomeofprivatehouseholdsis in 2010, significantly lower than in the metropolitan area of derived by adding transfers and subtracting taxes Tuxtla Gutiérrez (0.50) (Figure 4.24). from the market income. The market income of private households is composed by wages and salaries, income from capital and private transfers, Source such as remittances, private pensions, etc. Taxes include personal income taxes and social security OECD (2015), OECD Regional Statistics (database), http:// contributions by the employees. On the other hand, dx.doi.org/10.1787/region-data-en. transfers include direct cash and in-kind transfers, such as cash transfers, free food transfers, school Boulant, J., M. Brezzi and P. Veneri (forthcoming) “Income feeding programmes, etc. levels and inequality in metropolitan areas: a comparative approach in OECD countries”, OECD Regional Development Disposable income per equivalent household is expressed in USD purchasing power parities (PPP) at Policy Working Papers. constant prices (year 2010). The equivalence scale See Annex B for data sources and country-related metadata. consists in dividing household income by the square root of the average household size. The Gini coefficient is a measure of inequality among Reference years and territorial level all inhabitants of a given metropolitan area (see Annex C for the formula). The index takes on values Disposable income per equivalent household: Mexico, 2010; between 0 and 1, with zero interpreted as a situation Australia and France, 2011; Austria and United Kingdom, in which the income of all households is the same (no 2012; Estonia, Finland and United States, 2014. inequality). Gini coefficient for household disposable income: Mexico 2010; France, 2011; Austria, 2012; United States, 2014.

Household income in metropolitan areas

4.23. Metropolitan and non metropolitan disposable income per equivalent household, 2013

Metropolitan areas Rest of the country

USD 50 000

45 000

40 000

35 000

30 000

25 000

20 000

15 000

10 000

5 000

0 NOR USA AUS SWE FRA NLD FIN AUT GBR BEL DNK ITA JPN EST CHL HUN MEX 1 2 http://dx.doi.org/10.1787/888933364034

4.24. Minimum and maximum Gini coefficients for household disposable income in metropolitan areas, 2013 0.6

0.55 Tuxtla Gutiérrez

0.5 Santiago

0.45 Calgary Miami Brussels 0.4 Paris

0.35 Reynosa Concepción Malmö Milano

0.3 Copenhagen Graz Oslo Toulon

0.25 Albany Catania Antwerp Quebec Linz Göteborg 0.2 CAN (11) USA (70) MEX (33) BEL (4) CHL (3) FRA (15) SWE (3) ITA (11) AUT (3) DNK (1) NOR (1)

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ANNEX A

Defining regions and functional urban areas

Table A.1. Territorial grid of OECD member countries

Territorial level 2 Non-official grid Territorial level 3 Region (TL2) (NOG) (TL3)

AUS Australia States/territories (8) - Statistical Areas Level 4 and Greater Capital City Statistical Area (49) AUT Austria Bundesländer (9) - Gruppen von Politischen Bezirken (35) BEL Belgium Régions (3) - Arrondissements (44) CAN Canada Provinces and territories (13) LFS, Economic areas Census divisions (294) (71) CHL Chile Regions (15) Provincias (54) CZE Czech Republic Oblasti (8) - Kraje (14) DNK Denmark Regioner (5) - Landsdeler (11) EST Estonia Region (1) - Groups of maakond (5) FIN Finland Suuralueet (5) - Maakunnat (19) FRA France Régions (22) - Départements (96) DEU Germany Länder (16) Spatial planning Kreise (412) regions (96) GRC Greece Development regions (13) - Regional units and combination of regional units (51) HUN Hungary Planning statistical regions (7) - Counties + Budapest (20) ISL Iceland Regions (2) - Landsvaedi (8) IRL Ireland Groups regional authority regions (2) - Regional authority regions (8) ISR Israel Districts (6) - - ITA Italy Regioni (21) - Province (110) JPN Japan Groups of prefectures (10) - Prefectures (47) KOR Korea Regions (7) - Special city, metropolitan area and province (16) LUX Luxembourg State (1) - State (1) MEX Mexico Estados (32) - Grupos de municipios (209) NDL Netherlands Provinces (12) - COROP regions (40) NZL New Zealand Regional councils (14) - Regional councils (14) NOR Norway Landsdeler (7) - Fylker (19) POL Poland Vojewodztwa (16) - Podregiony (66) PRT Portugal Comissaoes de coordenaçao e desenvolvimento - Grupos de municipios (30) regional + regioes autonomas (7) SVK Slovak Republic Zoskupenia krajov (4) - Kraj (8) SVN Slovenia Kohezijske regije (2) - Statisti ne regije (12) ESP Spain Comunidades autonomas (19) - Provincias (59) SWE Sweden Riksomraden (8) - Län (21) CHE Switzerland Grandes regions (7) - Cantons (26) TUR Turkey Regions (26) - Provinces (81) GBR United Kingdom Regions and countries (12) - Upper tier authorities or groups of lower tier authorities or groups of unitary authorities or LECs or groups of districts (139) USA United States States and the District of Columbia (51) - Economic areas (179)

137 ANNEX A

Table A.2. Territorial grid of selected emerging economies

Country Territorial levels 2 (TL2) Territorial levels 3 (TL3)

Brazil Estados + distrito federal (27) Mesoregiao (137) China 31 provinces; special administrative region - of Hong Kong, China special administrative region of Macao, China and Chinese Taipei (33) Colombia Departamentos + Capital District (33) - India States and union territories (35) - Latvia Region (1) Statistical regions (6) Lithuania Region (1) Counties (10) Peru Departamentos + Provincia Constitucional del Callao (25) - Russian Federation Oblast or okrug (83) - South Africa Provinces (9) -

Table A.3. Smallest and largest regional population and population density by country

Number Region with the highest Region with the lowest Numbe Region with the highest Region with the lowest Country of TL3 of TL2 regions Population Density Population Density regions Population Density Population Density

Australia 49 4 840 628 444.5 37 800 0.1 8 7 518 472 164.3 245 079 0.2 Austria 35 1 765 575 4 469.8 20 451 20.3 9 1 765 575 4 469.8 287 318 57.7 Belgium 44 1 183 841 7 353.1 46 784 44.9 3 6 429 064 7 353.1 1 183 841 213.7 Canada 294 2 808 503 4 456.5 573 0.00 13 13 678 740 25.9 36 510 0.02 Chile 54 5 452 548 2 685.6 2 687 0.1 15 7 228 581 469.3 107 334 1.0 Czech Republic 14 1 302 336 2 561.2 300 309 66.2 8 1 680 287 2 561.2 1 125 429 70.9 Denmark 11 851 769 4 360.7 40 305 59.5 5 1 749 405 687.1 581 057 73.8 Estonia 5 572 103 132.0 124 684 13.2 1 1 315 819 30.3 1 315 819 30.3 Finland 19 1 585 473 174.3 28 666 2.0 5 1 585 473 174.3 28 666 6.4 France 96 2 595 539 21 265.1 76 543 14.8 22 12 005 077 999.4 323 092 37.2 Germany 412 3 421 829 4 531.2 34 084 35.6 16 17 571 856 3 854.7 657 391 68.8 Greece 51 3 856 059 1 013.2 19 902 11.0 13 3 856 059 1 013.2 198 109 30.1 Hungary 20 1 744 665 3 322.5 198 392 52.3 7 2 965 413 428.8 917 492 64.8 Iceland 8 208 752 200.2 6 972 0.6 2 208 752 200.2 116 919 1.2 Ireland 8 1 271 557 1 386.6 290 143 32.1 2 3 372 718 92.9 1 232 783 38.4 Israel - - - - - 6 1 976 300 7 740.1 951 900 82.4 Italy 110 4 321 244 2 691.4 57 699 31.3 21 9 973 397 438.3 128 591 39.7 Japan 47 13 390 000 6 992.2 574 000 64.7 10 35 922 000 2 739.6 3 878 000 64.7 Korea 16 12 280 678 16 334.7 581 069 90.6 7 25 029 687 2 138.3 581 069 90.6 Luxembourg 1 549 680 212.6 549 680 212.6 1 549 680 212.6 549 680 212.6 Mexico 209 8 360 233 7 525.0 9 167 0.8 32 16 618 928 5 980.3 710 986 10.0 Netherlands 40 1 417 821 3 301.1 47 958 144.7 12 3 577 032 1 273.8 380 621 185.3 New Zealand 14 1 526 900 341.2 32 800 1.4 14 1 526 900 341.2 32 800 1.4 Norway 19 634 463 1 488.3 75 207 1.6 7 1 210 220 241.8 382 253 4.5 Poland 66 1 719 692 3 326.3 267 900 43.0 16 5 292 567 368.8 960 226 57.7 Portugal 30 2 026 481 1 560.9 39 251 14.5 7 3 644 195 935.3 247 440 23.5 Slovak Republic 8 818 916 301.2 557 608 69.5 4 1 836 664 301.2 618 380 82.8 Slovenia 12 546 314 214.5 42 983 36.6 2 1 079 655 122.3 981 430 89.1 Spain 59 6 378 297 6 259.0 10 603 9.0 19 8 388 875 6 259.0 83 870 26.3 Sweden 21 2 163 042 331.4 57 161 2.6 8 2 163 042 331.4 368 617 3.4 Switzerland 26 1 425 538 5 117.2 15 778 27.4 7 1 808 480 858.2 346 539 100.3 Turkey 81 14 160 467 2 725.2 75 620 11.5 26 14 160 467 2 725.2 763 570 26.1 United Kingdom 139 2 241 058 10 656.5 21 618 7.1 12 8 821 331 5 389.3 1 835 847 68.5 United States 179 23 708 665 615.0 82 488 0.5 51 38 802 500 4 144.0 584 153 0.5

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Table A.4. Percentage of national population living in predominantly urban, intermediate and predominantly rural regions (TL3) and number of regions classified as such in each country

Percentage of population (2014) Number of regions (TL3)

Rural Intermediate Urban Rural Intermediate Urban (%) (%) (%)

Australia 19.7 10.1 70.2 29 11 9 Austria 44.1 20.8 35.1 25 5 5 Belgium 8.6 23.6 67.8 13 13 18 Canada 27.4 16 56.6 229 35 30 Chile 35.9 15.7 48.4 41 7 6 Czech Republic 32.9 42.9 24.2 6 6 2 Denmark 28.9 48.8 22.4 4 5 2 Estonia 45.2 11.4 43.5 3 1 1 Finland 40.4 30.5 29.1 12 6 1 France 30.6 34.6 34.8 55 27 14 Germany 16.3 42 41.7 118 204 90 Greece 43.8 10.6 45.7 44 5 2 Hungary 46.7 35.6 17.7 13 6 1 Iceland 35.8 64.2 - 7 1 0 Ireland 72.4 - 27.6 7 0 1 Italy 20.1 43 36.9 41 50 19 Japan 12 31.4 56.7 13 22 12 Korea 17.2 13.1 69.6 5 3 8 Luxembourg - 100 - 0 1 0 Mexico 36.6 17.4 46 145 30 34 Netherlands 0.6 26.9 72.5 1 17 22 New Zealand - 55.2 44.8 0 12 2 Norway 32.3 44 23.7 10 7 2 Poland 33.2 38.5 28.3 24 26 16 Portugal 20.1 26.9 53 15 8 7 Slovak Republic 50.2 38.4 11.4 4 3 1 Slovenia 43.4 56.6 - 7 5 0 Spain 7.3 33.5 59.2 14 29 16 Sweden 15.9 61.6 22.6 8 12 1 Switzerland 7.4 54.3 38.3 4 16 6 Turkey 30.2 36.4 33.4 49 27 5 United Kingdom 2.9 23.2 73.9 13 37 89 United States 37.7 20.2 42.1 132 21 26

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Figure A.1. Extended regional typology

Extended regional typology

Population share 15% < population share Population share in local rural areas < 15% in local rural areas < 50% in local rural areas > 50%

Urban centre with Urban centre with YESmore than YES more than 500 000 inhabitants > 25% 200 000 inhabitants > 25% regional population regional population

NO NO

OECD Predominantly urban (PU) Intermediate (IN) Predominantly rural (PR) regional typology

Driving time (DT) Driving time (DT) of at least 50% of of at least 50% of the regional population the regional to the closest locality population to the closest with more than locality with more than 50 000 inhabitants 50 000 inhabitants

DT < 60 min DT > 60 min DT < 60 min DT > 60 min

IN close to a city (INC) IN remote (INR) PR close to a city (PRC) PR remote (PRR)

Figure A.2. Extended regional typology: Americas (TL3)

Predominantly urban Intermediate Predominantly rural Predominantly rural close to a city* Predominantly rural remote Data not available

*The methodology to distinguish between rural regions close of a city and remote rural regions has not been applied to Australia, Chile, Iceland, Korea and New Zealand. This map is for illustrative purposes and is without prejudice to the status of or sovereignty over any territory covered by this map. Source of administrative boundaries: National Statistical Ofﬁces and FAO Global Administrative Unit Layers (GAUL).

1 2 http://dx.doi.org/10.1787/888933364058

Figure A.3. Extended regional typology: Europe (TL3)

Predominantly urban Intermediate Predominantly rural Predominantly rural close to a city* Predominantly rural remote Data not available

*The methodology to distinguish between rural regions close to a city and remote rural regions has not been applied to Australia, Chile, Iceland, Korea and New Zealand. This map is for illustrative purposes and is without prejudice to the status of or sovereignty over any territory covered by this map. Source of administrative boundaries: National Statistical Ofﬁces and FAO Global Administrative Unit Layers (GAUL).

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Figure A.4. Extended regional typology: Asia and Oceania (TL3)

Predominantly urban Intermediate Predominantly rural Predominantly rural close to a city* Predominantly rural remote Data not available

* The methodology to distinguish between rural regions close of a city and remote rural regions has not been applied to Australia, Chile, Iceland, Korea and New Zealand. This map is for illustrative purposes and is without prejudice to the status of or sover-eignty over any territory covered by this map. Source of administrative boundaries: National Statistical Ofﬁces and FAO Global Administrative Unit Layers (GAUL).

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Figure A.5. Methodology to define the functional urban areas

1. Apply a threshold to identify densely populated grid cells

Europe Japan Canada Korea ≥ ≥ 1 500 inhabitants 1 000 United States Chile per km2 inhabitants per km2 Mexico Australia

2. Identify contiguous high-density urban clusters STEP 1: Identification

of city centres Europe United States Japan Chile ≥ 50 000 inhabitants ≥ 100 000 inhabitants Korea Canada Mexico Australia

3. Identify core municipalities

If 50% of the population of the municipality lives within the high-density cluster

STEP 2: Connecting Two or more city centres will belong to the same functional urban area non-contiguous city centres belonging If more than 15% of the population of one city centre to the same commutes to work to another city centre functional area

STEP 3: Individual municipalities will be part of the “working catchment area” Identifying the commuting zone If more than 15% of its population commutes to work in the city centre

RESULTS Monocentric Polycentric functional urban areas functional urban areas (with only one city centre) (with more than one city centre)

Table A.5. Number of Metropolitan areas and share of national population in metropolitan areas, 2014 Metropolitan areas (functional urban areas with population above 500 000)

Population between Population Rest Total metropolitan areas 500 000 and 1.5 million above 1.5 million (non metropolitan) Country % of national % of national % of national % of national Number Number Number population population population population

AUS Australia 6 65.7 2 7.9 4 57.8 34.3 AUT Austria 3 47.5 2 14.7 1 32.8 52.5 BEL Belgium 4 44.6 3 21.5 1 23.1 55.4 CAN Canada 9 57.2 6 18.3 3 38.9 42.8 CHL Chile 3 48.3 2 10.8 1 37.5 51.7 CZE Czech Republic 3 29.7 2 11.5 1 18.2 70.3 DNK Denmark 1 36.0 - - 1 36.0 64.0 EST Estonia 1 40.3 1 40.3 - - 59.7 FIN Finland 1 27.5 1 27.5 - - 72.5 FRA France 15 40.7 12 16.0 3 24.7 59.3 DEU Germany 24 39.4 18 18.6 6 20.8 60.6 GRC Greece 2 41.4 1 9.0 1 32.4 58.6 HUN Hungary 1 29.2 - - 1 29.2 70.9 IRL Ireland 1 39.9 1 39.9 - - 60.1 ITA Italy 11 30.5 7 8.1 4 22.5 69.5 JPN Japan 36 69.5 30 17.4 6 52.1 30.5 KOR Korea 10 76.0 7 14.3 3 61.7 24.0 MEX Mexico 33 52.1 26 20.2 7 31.9 47.9 NLD Netherlands 5 37.6 4 23.0 1 14.6 62.4 NOR Norway 1 25.5 1 25.5 - - 74.6 POL Poland 8 30.2 6 15.4 2 14.8 69.8 PRT Portugal 2 40.3 1 12.6 1 27.7 59.7 SVK Slovak Republic 1 13.5 1 13.5 - - 86.5 SVN Slovenia 1 28.4 1 28.4 - - 71.6 ESP Spain 8 37.8 6 14.3 2 23.5 62.2 SWE Sweden 3 37.3 2 16.3 1 20.9 62.7 CHE Switzerland 3 35.1 3 35.1 - - 64.9 GBR United Kingdom 15 41.1 12 15.7 3 25.3 58.9 USA United States 70 54.7 42 13.9 28 40.8 45.3

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ANNEX B

Sources and data description

User guide: List of variables

Variables used Page Chapter(s)

Area 147 2 Age-adjusted mortality rates based on mortality data 148 1 Death rates due to diseases of the respiratory system 148 1 Employment at place of work and gross value added by industry 149 2 Gini index of household disposable income 149 1 Gross domestic product (GDP) 150 2 Homicides 151 1 Hospital beds 152 4 Household disposable income 153 1 Households with broadband connection 154 4 Housing expenditures as a share of household disposable income 155 1 Individuals with unmet medical needs 155 1 Labour force, employment at place of residence by gender, unemployment, total and growth 156 1 and 2 Labour force by educational attainment 158 1 Life expectancy at birth, total and by gender 159 1 Life satisfaction 159 1 Local governments in metropolitan areas 160 2 Metropolitan population, total and by age 161 2 Motor vehicle theft 162 1 Municipal waste and recycled waste 163 4 Number of rooms per person 163 1 Part-time employment 164 4 Perception of corruption 164 1 PCT patent and co-patent applications, total and by sector 165 2 Physicians 165 4

PM2.5 particle concentration 166 1 Population, total, by age and gender 166 2 Population mobility among regions 167 4 R&D expenditure 169 2 R&D personnel 170 2 Social network support 170 1 Subnational government expenditure, revenue, investment and debt 171 3 Voter turnout 171 1 Young population neither in employment nor in education or training 172 4 Youth unemployment 173 4

The tables refer to the years and territorial levels used in this publication. The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

Area

Source

EU23 countries1 Eurostat: General and regional statistics, demographic statistics, population and area Australia Australian Bureau of Statistics (ABS), summing up SLAs Canada Statistics Canada http://www12.statcan.ca/english/census01/products/standard/popdwell/Table-CD- P.cfm?PR=10&T=2&SR=1&S=1&O=A Iceland Statistics Iceland Israel Central Bureau of Statistics – Statistical Abstract of Israel. Japan Statistical Office, Area by Configuration, Gradient and Prefecture www.stat.go.jp/English/data/nenkan/1431- 01.htm Korea Korea National Statistical Office Mexico Mexican Statistical Office (INEGI) New Zealand Statistics New Zealand, data come from the report “Water Physical Stock Account 1995–2005” Norway Statistics Norway, StatBank table: Table: 09280: Area of land and fresh water (km²) (M) Switzerland Office fédéral de la statistique, ESPOP, RFP Turkey Eurostat: General and regional statistics, demographic statistics, population and area United States Census Bureau www.census.gov/population/www/censusdata/density.html Brazil Instituto Brasileiro de Geografia e Estadística (IBGE) China National Bureau of Statistics of China India Statistics India (Indiastat) Russian Federation Federal State Statistics Service of Russian Federation South Africa Statistics South Africa

Age-adjusted mortality rates based on mortality data

Source Year Territorial level

Australia Australian Bureau of Statistics; Table 3302.0 2012 2 Austria Statistics Austria 2013 2 Belgium Federal Public Service Economy/Statistics Belgium 2013 2 Canada1 Statistics Canada; CANSIM, Table 051-0002 2013 2 Chile INE 2012 2 Czech Republic Institute of Health Information and Statistics of the Czech Republic 2013 2 Denmark Statistics Denmark; Table FOD207 2013 2 Estonia Statistics Estonia; Table PO052 2013 3 Finland Statistics Finland 2013 2 France Insee 2013 2 Germany Federal Office of Germany and the Statistical Offices of the Federal States 2013 2 Greece Hellenic Statistical Authority 2013 2 Hungary Hungarian Central Statistical Office 2013 2 Iceland Statistics Iceland 2013 2 Ireland CSO; Table VSA07 2013 2 Israel Central Bureau of Statistics Israel 2013 2 Italy Istat; Table P.5 2013 2 Japan Statistics Bureau of Japan, MIC 2013 2 Korea Statistics Korea 2013 2 Mexico National Institute of Statistics and Geography (INEGI) 2012 2 Netherlands2 Eurostat regional statistics; Table demo_r_pjangrp3 2013 2 New Zealand Statistics New Zealand 2013 2 Norway Statistics Norway; Table 01222 and 08426 2013 2 Poland Central Statistical Office of Poland 2013 2 Portugal Statistics Portugal 2013 2 Slovak Republic Statistical Office of the SR 2013 2 Slovenia Statistical Office of the Republic of Slovenia 2013 2 Spain INE 2013 2 Sweden Statistics Sweden 2013 2 Switzerland Swiss Federal Statistical Office; Table BEVNAT 2013 2 Turkey Turkish Statistical Institute 2013 2 United Kingdom3 Eurostat regional statistics; Table demo_r_pjangrp3 2013 2 United States US Centers for Disease Control and Prevention 2013 2 Brazil Ministry of Health 2013 2 Colombia DCD 2013 2 Latvia2 Eurostat regional statistics; Table demo_r_pjangrp3 2013 3 Peru Ministry of Health 2013 2 Russian Federation Federal State Statistics Service 2013 2

1. Canada: Stillbirths are excluded. Data refer to the age attained at the last birthday preceding death. 2. Data refer to the age reached during the year. 3. United Kingdom: Data refer to the age in completed years.

Death rates due to diseases of the respiratory system

Source Years Territorial Level

EU17 Eurostat (2015) Deaths from diseases of the respiratory system by NUTS 2, 2010 2 countries1 + Switzerland crude death rates per 100 000 inhabitants http://ec.europa.eu/eurostat/data/ database.

1. Europe17 countries: Austria, Czech Republic, Germany, Spain, Finland, France, Greece, Hungary, Ireland, Italy, the Netherlands, Norway, Poland, Portugal, Slovak Republic, Slovenia, Sweden.

Employment at place of work and gross value added by industry (ISIC rev. 4)

Source Years Territorial level

EU23 countries1 Eurostat, Regional economic accounts, Branch accounts, Employment 2000-13 2 Australia2 Australian Bureau of Statistics, cat. no. 5220.0 – Australian National 2000-13 2 Accounts: State Accounts, and Table 6291.0.55.003 Labour Force Canada Statistics Canada. CANSIM database, Tables 379-0028 Gross domestic 2002-12 2 product (GDP) at basic prices and 282-0008 Labour force survey estimates (LFS), by North American Industry Classification System Chile Banco Central de Chile 2013 2 Iceland n.a. - - Israel n.a. - - Japan Statistics Bureau, Economically Active Population Survey & Local Area 2009-12 2 Labour Force Survey Korea Korean National Statistical Office – KOSIS Census on basic characteristics 2004-12 2 of establishments Mexico INEGI. Consulta interactiva de datos www.inegi.org.mx/sistemas/olap/ 2013 2 proyectos/bd/consulta.asp?p=16859&c=17383&s=est&cl=3#. New Zealand Statistics New Zealand. Gross domestic product by industry, per region 2000-12 2 Norway Eurostat, Regional economic accounts, Branch accounts, Employment 2013 2 Switzerland Federal Statistical Office FSO. Gross value added (GVA) by canton and 2002-2012 2 industries (je-e-04.06.02) and Swiss Labour Force Survey – SLFS Turkey Turkish Statistical Institue (TurkStat). Employment data from the Household 2009-14 2 Labour Force Survey. No regional breakdown for GVA by industry. United States Bureau of Economic Analysis. Gross Value Added by State and employment 2000-12 2 by industry (SA25, SA25N)

Gini index of household disposable income (regional)

Source Years Territorial level

EU23 countries1 EU-SILC 2010-14 2 Canada Canadian Income Survey, 2013 reference income 2013 2 Chile Source: Encuesta de Caracterización Socioeconómica Nacional (CASEN), 2013 2013 2 Denmark Danish Law Model System 2013 2013 2 France ERFS, 2010 reference income 2010 2 Germany Socio-Economic-Panel (SOEP), 2013 reference income 2013 2 Israel Household expenditure survey 2014 2014 2 Japan Comprehensive Survey of Living Conditions, 2009 2009 2 Mexico Encuesta Nacional de Ingreso y Gastos des Hogares 2014 2014 2 Netherlands Income Panel Survey, 2014 2014 2 Norway Income and Wealth Statistics for Household, 2014 reference income 2014 2 New Zealand Household economic survey, 2011 reference income 2011 2 regions Sweden Swedish Household Income Survey, 2013 reference income 2013 2 Turkey Turkish SILC, 2013 reference income 2013 2 United Kingdom Households Below Average Income, average for 2010-2012 2010-12 2 United States CPS ASEC (redesigned), average for 2013-14 reference income 2013-14 2

1. Belgium, Czech Republic, Greece, Hungary (NUTS1), Ireland, Poland (NUTS1) and Slovak Republic: EU-SILC, 2014 wave (2013 reference income) ; Austria and Spain: EU-SILC, 3-year average 2011-13 reference income ; Slovenia and Switzerland: EU-SILC, 2011 wave (2010 reference income) ; Finland : EU-SILC, 2015 wave, 2014 reference income ; Italy: EU-SILC, 3-year average 2012-14 reference income.

Gross domestic product

Source Years Territorial level

2, 3 EU23 countries1, 3, 5 Eurostat, Regional economic accounts 2000-13 and metropolitan areas 2 Australian Bureau of Statistics, 5220.0. Gross state product, figures based Australia3 2000-13 and metropolitan on fiscal year (July-June). areas 2 Canada3 Statistics Canada, Provincial economic accounts 2000-13 and metropolitan areas 2 Chile2, 3 Banco central de Chile. Cunetas nacionales de Chile 2000-13 and metropolitan areas Iceland4 n.a. - - Israel4 n.a. - - 2, 3 Economic and Social Research Institute, Cabinet Office, data are based on and metropolitan Japan3, 5 2000-13 fiscal year (April-March). areas and metropolitan areas 2, 3 Korea3 Korean National Statistical Office 2000-13 and metropolitan areas 2 Mexico3 INEGI, System of national accounts of Mexico 2000-13 and metropolitan areas New Zealand Statistics New Zealand 2000-13 2,3 2, 3 Norway3, 5 Norwegian Regional Accounts 2008-12 and metropolitan areas 2, 3 Switzerland3, 5 Swiss Federal Statistical Office, Statweb 2008-12 and metropolitan areas Turkey Turkish Statistical Institute (TurkStat), no data available after 2001 - 2 2 United States3 Bureau of Economic Analysis 2000-13 and metropolitan areas Brazil Instituto Brasileiro de Geografia e Estadística (IBGE) 2000-12 2 China National Bureau of Statistics of China 2004-12 2 Colombia Departamento Administrativo Nacional de Estadistica 2001-10 2 India Statistics India (Indiastat) 2004-10 2 Indonesia Statistics Indonesia. 2004-12 2 Russian Federation Federal State Statistics Service of Russian Federation 2000-12 2 South Africa Statistics South Africa 2000-13 2

1. EU23 countries: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden and the United Kingdom. Due to break in series generated by the change in SNA classification, the Secretariat made estimates for Belgium (2000-08), Germany (2000-09), Italy (2000-10) and Netherlands (2000-09) based on previous data releases from Eurostat. 2. Chile: to allow comparison across time, from 1995 to 2010 Tarapacá includes Arica Y Parinacota, and Los Lagos includes Los Rios. Data are not available in two regions. 3. Available years at metropolitan level: Austria, Germany, Estonia, Spain, Finland, France, Hungary, Ireland, Italy, Poland and Sweden 2000-12; Switzerland and Norway 2008-12; Japan 2001-12; United States 2001-13, Mexico 2003-13; Belgium, Czech Republic, Denmark, Greece, Netherlands, Portugal, Slovenia, Slovak Republic, United Kingdom, Korea, Canada, Chile, Australia 2000-13. GDP estimates at metropolitan areas level were based on TL3 data with the exception of Germany where the NOG were used; Australia, Belgium, Canada, Chile, Greece, Mexico and the Netherlands where TL2 data were used. Metropolitan figures for the United States were provided by the U.S. Bureau of Economic Analysis. The methodology to estimate GDP figures at metropolitan level is described in Annex C. 4. Iceland and Israel: Data not available at the regional level. 5. Available years at TL3 level: Austria, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Poland, Spain, Sweden 2000-12; Japan 2001-12; Lithuania 2005 12; Norway and Switzerland and Norway 2008-12.

Homicides

Source Years Territorial Level

Australia Australian Bureau of Statistics (ABS), Recorded Crime – Victims, Australia, 2013 2 2013 (cat. no. 4510.0) Austria Austria Home Office, Crime Statistics 2013 2 Belgium Belgian Federal Police 2013 2 Canada Statistics Canada. CANSIM database Table 253-0001 – Homicide Survey, 2012 2 Canadian Centre for Justice Statistics Chile1 INE, Chile. Undersecretariat of Crime Prevention, Ministry of Interior 2012 2 and Public Safety Czech Republic Czech Statistical Office; Police of the Czech Republic 2013 2 Denmark2 Statistics Denmark, StatBank Table STRAF11: Reported criminal offences, 2013 2 Homicide series Finland Statistics Finland, Justice statistics 2013 2 France INSEE, Etat 4001 annuel, DCPJ 2012 2 Estonia3 OECD Regional Questionnaire; information provided by the delegate 2013 3 of the Working Party of Territorial Indicators (WPTI) Germany OECD Regional Questionnaire; information provided by the delegate 2010 2 of the Working Party of Territorial Indicators (WPTI) Greece Hellenic Statistical Authority, Hellenic Police (offences committed)/ 2013 2 completed and attempted action Hungary Ministry of Justice, Chief Prosecutor's Department 2013 2 Iceland OECD Regional Questionnaire; information provided by the delegate 2012 2 of the Working Party of Territorial Indicators (WPTI) Ireland CSO, StatBank Ireland, Table CJQ02: Recorded Crime Offences by Garda 2013 2 Region Israel9 Central Bureau of Statistics Israel 2013 2 Italy4 ISTAT, crimes reported by the police forces to the judicial authority 2013 2 Japan Criminal Statistics in 2014, National Police Agency, Publications of the Police 2014 2 Policy Research Center Korea Korean Ministry of Justice 2013 2 Mexico5 Directorate General of Government of Mexico, Public Safety and Justice 2014 2 Statistics Netherlands Statistics Netherlands (CBS)-STATLINE 2012 2 New Zealand Statistics New Zealand, Annual Recorded Offences for the latest Calendar 2014 2 Years (ANZSOC) Norway Directorate of the Police of Norway (homicides) and Statistics Norway 2013 2 (crime against property) Poland6 National Police Headquarters 2011 2 Portugal7 Ministry of Justice – Directorate-General for Justice Policy 2013 2 Slovak Republic Statistical Office of the Slovak Republic, regional database Datacube 2013 2 Slovenia OECD Regional Questionnaire; information provided by the delegate 2012 2 of the Working Party of Territorial Indicators (WPTI) Spain INE 2013 2 Sweden Swedish National Council for Crime Prevention (Brå) 2013 2 Switzerland8 Federal Statistical Office (FSO), Police crime statistics 2013 2

Homicides (cont.)

Source Years Territorial Level

Turkey General Directorate of Security, General Commandership of Gendarme 2013 2 United Kingdom ONS, Crime and Justice, Table 04, Police Force Area Data Tables – Crime in 2013 2 England and Wales, Year Ending December 2013 United States Federal Bureau of Investigation, Crime in the United States, Table 4 2013 2

1. Figures are people who have been the victim of murder. Data based on crimes known by one police force (Carabineros de Chile). 2. Reported criminal offences. 3. In some cases the distribution of indicators by regions is unknown. Therefore the sums of regions are not always equal with the total for Estonia. 4. In a few cases, when it's hard to identify the exact place where a crime is committed, the sum of provincial data doesn't equal the regional total data (the latter including more crimes). 5. National Census 2012 State Law Enforcement. As part of the implementation of the National Census of Law Enforcement 2011 and 2012, the figure provided for 2010 and 2011 corresponds to the data of the relevant offenses, registered preliminary enquiries initiated by the Public Prosecutor of the Common Jurisdiction in each of the federal states. 6. Data have been revised. They include ascertained crimes from the category of homicide and infanticide in any form. 7. Murders account for surveys of the judicial police coming out with proposed charges for the crime of murder consummated. 8. From 2009, police statistics on crime have been revised and are thus not comparable to the old police statistics; this translates into a break in series between 2008 and 2009. 9. The police districts are different from CBS districts, Northern district data Includes Haifa District. Some files are not included in the districts data when they are managed at the national level. Homicides data includes acts of terrorism.

Hospital beds

Source Year Territorial Level

EU23 countries1 Eurostat, available beds in hospitals (tgs00064) 2013 2 Australia2 Australian Institute of Health and Welfare (AIHW), for Public Hospitals, 2012 2 Table 4.1. ABS; for Private Hospitals cat. no. 4390.0) Canada Canadian MIS Database (CMDB), CIHI 2010 2 Chile INE, Chile. Department of Health Statistics and Information (DEIS), 2009 2 Ministry of health (MINSAL) Iceland n.a. - - Israel Central Bureau of Statistics Israel, Ministry of Health of Israel 2012 2 Japan Statistics Bureau, Survey of Medical Institutions, MHLW Japan 2010 2 Korea n.a. - - Mexico Statistics Private Health Establishments. INEGI. 2011 2 Bulletin of Statistical Information. Secretariat for Health (SS) New Zealand n.a. - - Norway Eurostat, available beds in hospitals (tgs00064) 2013 2 Switzerland Federal Statistical Office (FSO), Neuchâtel; Swiss Medical Association 2013 2 (FMH), Bern; Medical Statistics of Physicians, yearly census Turkey3 Ministry of Health, General Directorate of Health Research, 2013 2 Health Statistics Yearbook United States4 US Center for Disease Control and Prevention 2009 2

Household disposable income

Source Years Territorial level

EU21 countries1 Eurostat, Household income statistics, primary and disposable income 1995-13 2 Australian Bureau of Statistics, Australian National Accounts, Household 2 and metropolitan Australia 1995-13 Income Account (cat. no. 5220.0 table 12). Gross disposable income series areas Austria Statistics Austria 2012 Metropolitan areas Belgium Statistics Belgium 2013 Metropolitan areas Statistics Canada. CANSIM database. Table 384-0040 – Current accounts – 2 and metropolitan Canada 1995-13 Households, provincial and territorial areas 2 and metropolitan Chile3 National Socio-economic Survey (CASEN), 1996-12 areas Denmark Statistics Denmark 2013 Metropolitan areas Estonia Statistics Estonia 2014 Metropolitan areas Finland Statistics Finland 2014 Metropolitan areas France Insee 2011 Metropolitan areas Hungary Hungarian Ministry for National Economy 2013 Metropolitan areas Iceland2 n.a. - - Israel Central Bureau of Statistics- Income Survey 1996-11 2 Italy Ministry of Economy and Finance 2013 Metropolitan areas Statistics Bureau of Japan 2001-12 2 and Metropolitan Japan3 Ministry of Internal Affairs and Communications 2013 areas Korea Statistics Korea, KOSIS database – Korean Regional Accounts 2010-13 2 INEGI, Household Income and Expenditure National Survey Socioeconomic 2 and metropolitan Mexico2 2008-14 Conditions Module (MCS) areas Netherlands Statistics Netherlands 2013 Metropolitan areas New Zealand3 Statistics New Zealand. Household income by region 1998-13 2 2 and metropolitan Norway Statistics Norway, Regional Accounts. Table: 09797: Households' income 2011-13 areas Sweden Statistics Sweden 2013 Metropolitan areas Switzerland2 n.a. - - Turkey2 Turkish Statistical Institue (TurkStat) 2014 2 United Kingdom Office for National Statistics 2012 Metropolitan areas U.S. Bureau of Economic Analysis, Table SA51 Disposable Personal Income 1995-14 2 and Metropolitan United States American Community Survey 2014 areas

The disposable income of private households is derived from the balance of primary income by adding all current transfers from the government, except social transfers in kind and subtracting current transfers from the households such as income taxes, regular taxes on wealth, regular inter-household cash transfers and social contributions. The disposable income of households does not take into account social transfer in kind to households. A preferable measure of material condition of households at regional level could be the adjusted disposable income which additionally reallocates income from government and non-profit institutions serving the households, through expenditure on individual goods and services such as health, education and social housing (in-kind expenditure). Interregional disparities of adjusted household income could shed a light on possible areas of social exclusion, material deprivation and lack of access to essential services. 1. EU21 countries: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Netherlands, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden and the United Kingdom. Denmark, Austria: 2000-13; Slovenia: 1999-2012; Belgium, France, Netherlands and Spain: 1995-2011; Finland, Hungary and Sweden: 2000-12; Ireland: 1996-2012; Germany and Italy: 1995-2012; Poland: 2010-12; Portugal: 2000-11; United Kingdom: 1997-2013; Estonia: 2008-13; Slovak Republic: 1996-2012. 2. Iceland and Switzerland: data are not available at the regional level. 3. Chile, Greece, Japan and New Zealand: primary income of households are not available at the regional level.

Households with broadband connection

Source Year Territorial Level

EU14 countries1 Eurostat, Regional information society statistics, Households with 2014 2 broadband access, table isoc_r_broad_h Australia Australian Bureau of Statistics (ABS), Household Use of Information 2013 2 Technology, Australia, 2012-13 (cat. no. 8146.0), Financial year Belgium Statistics Belgium, ICT indicators for households and individuals (2005-13) 2014 2 Canada Statistics Canada, CANSIM (database), Table 203 00272 Survey 2012 2 of household spending (SHS) Chile INE, Chile, National Statistical Institute 2012 2 Czech Republic Czech Statistical Office CZSO, Information technology survey 2014 2 Hungary HCSO, Hungarian Central Statistical Office 2014 2 Iceland Statistics Iceland. Internet connections and access devices in households 2012 2 2003-12, broadband connection Israel Central Bureau of Statistics Israel, Household expenditure survey, Table 16 2013 2 Japan Statistics Bureau, Ministry of Internal Affairs and Communications, Japan 2011 2 Korea Korean Ministry of Science, ICT and Future Planning – Survey on the 2014 2 Internet Usage (MSIP, KISA) Mexico INEGI-Módulo, Availability and Use of Information Technologies 2014 2 in Households (MODUTIH) New Zealand Statistics New Zealand: The household Use of Information 2012 2 and Communication Technology (ICT) Survey Poland Central Statistical Office of Poland 2014 2 Portugal Statistics Portugal (INE), Survey on Information and Communication 2014 2 Technologies Usage in Private Households Slovak Republic Statistical Office of the SR, ICT usage in households and by individuals 2014 2 Spain INE 2014 2 Switzerland Federal Statistical Office of Switzerland (FSO). 2006-11 : Enquête sur le 2014 2 budget des ménages (EBM) Société de l'information – Internet haut débit – Indicateur 30107 ; 2014 Omnibus TIC Turkey Eurostat, Regional information society statistics, Households 2013 2 with broadband access, table isoc_r_broad_h United States Census Bureau, American Community Survey (ACS), 1-year estimates, 2011 2 table S1501

1. EU14 refers to Austria, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Norway, Slovenia, Sweden, United Kingdom.

Housing expenditures as a share of household disposable income

Source Year Territorial level

Australia Australia Bureau Statisitics; Table 4130.0 2011 2 Austria Statistics Austria, EU-SILC 2013 2 Belgium Household Budget Survey 2012 2 Canada Statistics Canada; CANSIM, Table 203-0022 2012 2 Chile1 n.a. - - Czech Republic1 n.a. - - Denmark Statistics Denmark; Household Budget Survey, Table FU5 2012 2 Finland Statistics Finland; Table 140_ktutk_tau_104 2012 2 France1 n.a. - - Germany1 n.a. - - Greece1 n.a. - - Hungary Hungarian Central Statistical Office 2013 2 Iceland1 n.a. - - Ireland Household Budget Survey 2010 2 Israel Central Bureau of Statistics Israel 2013 2 Italy OECD estimates based on ISTAT – Household Budget Survey 2013 2 Japan OECD estimates based on Monthly spending on housing data, Table 11 2013 2 Korea1 n.a. - - Mexico1 n.a. - - Netherlands1 n.a. - - New Zealand Statistics New Zealand 2013 North/South Islands Norway OECD estimates based on Statistics Norway – Survey on Consumer 2012 2 Expenditure Poland Household Budget Survey 2013 2 Portugal Statistics Portugal, Household Budget Survey 2011 2 Slovak Republic Statistical Office of the SR, Household Budget Survey 2012 2 Slovenia1 n.a. - - Spain OECD estimates based on INE - Household Budget Survey; Table-10722 2011 2 Sweden1 n.a. - - Switzerland Household Budget Survey 2009-11 (3-year-pooled sample) 2010 2 Turkey Household Budget Survey 2013 2 United Kingdom Office for National Statistics; Table A35 2012 2 United States1 n.a. - -

1. Chile, Czech Republic, France, Germany, Greece, Iceland, Korea, Mexico, Netherlands, Slovenia, Sweden and United States: data not available at the regional level.

Individuals with unmet medical needs

Source Year Territorial Level

EU9 countries1 European Survey on Income and Living Conditions (EU-SILC) 2013 2 Chile Ministry of Social Development, Government of Chile, Encuesta de 2013 2 Caracterización Socioeconómica Nacional (Casen) Mexico Instituto Nacional de Salud Pública (INSP), Encuesta Nacional de Salud 2013 2 y Nutrición (ENSANUT) New Zealand Ministry of Health, New Zealand Health Survey 2013 2 Turkey European Survey on Income and Living Conditions (EU-SILC) 2013 2

1. EU9 refers to Austria, Czech Republic, Estonia, Finland, France, Greece, Italy, Spain and the United Kingdom.

Labour force, employment at place of residence by gender, unemployment (total and growth)

Source Year Territorial level

Australia5 Australian Bureau of Statistics; Table 6291.0.55.001 2001-14 2, 3 and metropolitan areas Austria2, 5 Statistics Austria, Labour Force Statistics Survey 2000-13 2, 3 and metropolitan areas Belgium1, 5 Eurostat, Labour Force 2000-14 2 and metropolitan areas Canada1, 5 Statistics Canada; CANSIM, Table 282-0002 2001-14 2, NOG and metropolitan areas Chile1, 5 INE, New National Employment Survey 2000-14 2 and metropolitan areas Czech Republic2, 5 Czech Statistical Office, Labour Force Survey 2000-13 2, 3 and metropolitan areas Denmark5 Statistics Denmark; Table RASA1 2000-14 2, 3 and metropolitan areas Estonia5 Statistics Estonia; Table ML243 (employment), Table ML4645 (labour force) 2000-14 3 and and Table ML50 (unemployment) metropolitan areas Finland5 Statistics Finland 2000-14 2, 3 and metropolitan areas France5, 6 Eurostat, Labour Force statistics for TL2 and Insee for TL3 2000-14 2, 3 and metropolitan areas Germany2, 5 Federal Employment Agency 2001-14 2, NOG and metropolitan areas Greece2, 5 Hellenic Statistical Authority, Labour Force Survey 2000-14 2, 3 and metropolitan areas Hungary5 Hungarian Central Statistical Office, Labour Force Survey 2000-14 2, 3 and metropolitan areas Iceland1 Statistics Iceland 2000-14 2 Ireland Eurostat, Labour Force statistics for Labour force and employment; CSO 2000-14 2 and 3 Table QNQ22 for unemployment Israel1, 2, 3 Central Bureau of Statistics Israel 2000-14 2 Italy5 ISTAT, Labour Force Survey 2000-14 2, 3 and metropolitan areas Japan5 Statistics Bureau of Japan, Labour Force Survey 2001-14 2, 3 and metropolitan areas Korea5 Statistics Korea, Economically Active Population Survey & Local Area 2000-14 2, 3 and metropolitan Labour Force Survey areas Luxembourg Eurostat, Labour Force 2000-13 2 Mexico1, 5 INEGI, National Survey of Occupation and Employment 2000-14 2 and metropolitan areas Netherlands2, 5 Eurostat, Labour Force 2000-14 2, 3 and metropolitan areas New Zealand4, 5 Statistics New Zealand, Household Labour Force Survey 2000-14 2 and 3 Norway1, 5 Statistics Norway 2000-14 2, 3 and metropolitan areas Poland2, 5 Central Statistical Office of Poland, Labour Force Survey 2000-14 2, 3 and metropolitan areas Portugal1, 5 Statistics Portugal, Labour Force Survey 2000-14 2 and metropolitan areas Slovak Republic5 Statistical Office of the SR, Labour Force Survey 2000-14 2, 3 and metropolitan areas Slovenia2, 5 Eurostat, Labour Force 2001-14 2, 3 and metropolitan areas Spain5 INE, Labour Force Survey 2000-14 2, 3 and metropolitan areas Sweden5 Statistics Sweden, Labour Force Survey 2000-14 2, 3 and metropolitan areas Switzerland1, 5 Federal Statistical Office of Switzerland, Structural Labour Force Survey 2000-14 2 and metropolitan areas Turkey1 TURKSTAT, Household Labour Force Survey Revised Results 2000-14 2

Labour force, employment at place of residence by gender, unemployment (total and growth) (cont.)

Source Year Territorial level

United Kingdom5 Office for National Statistics 2000-14 2, 3 and metropolitan areas United States5 U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics 2000-14 2, 3 and metropolitan program areas Brazil IBGE, National Survey by Household Sampling 2004-13 2 Colombia DANE, Great integrated Household Survey 2001-12 2 Latvia2 Eurostat, Labour Force Statistics 2000-14 3 Lithuania2 Eurostat, Labour Force Statistics 2000-14 3 Peru National Institute of Statistics and Informatics, National Household Survey 2001-14 2 Russian Federation Federal State Statistics Service, Labour force Survey 2000-14 2 South Africa Statistics South Africa; Quarterly Labour Force Survey, Table P0211 2000-14 2 1. Belgium, Canada, Chile, Iceland, Israel, Mexico, Norway, Portugal, Switzerland and Turkey: data not available at the territorial level 3. 2. Labour market statistics by urban-rural typology (urt_lmk) for unemployment at TL3 3. Israel: Data not available at the regional level. 4. New Zealand: Gisborne/Hawke's Bay combined (NZ016 included in NZ015), Tasman/Nelson/Marlborough/West Cost combined (NZ022 included in NZ021). 5. Available years at metropolitan level: Australia, Austria and the Czech Republic 2000-13; Slovenia 2001-11; Germany 2001-14; Denmark 2007-14; Switzerland 2007-13; Belgium, Estonia, Spain, Finland, France, Greece, Hungary, Ireland, Italy, Netherlands, Norway, Poland, Portugal, Sweden, Slovak Republic, United Kingdom, Mexico, Korea, Japan, Canada, United States and Chile 2000-14. Metropolitan labour figures are estimates based on labour data at TL3 level except for Belgium, Chile, Greece, Mexico, Netherlands, Poland and Portugal were TL2 data are used and NOG for Canada and Germany. Australia and United States figures are provided by the Australian Bureau of Statistics and the U.S. Bureau of Labour Statistics respectively. The methodology to estimate labour figures at metropolitan level is described in Annex C. 6. Regional values derived from the Labour Force Survey in France should be taken with caution due to the relatively small sample size.

Labour force by educational attainment

Source Year Territorial Level

EU21 countries1, 8, 9 Eurostat, Labour Force Survey, Regional education statistics 2000-14 2 plus Norway and Switzerland8 Australia2 Australian Bureaus of Statistics, Table 6227.0 Education and Work, LFS 2010-14 2 Canada3 Statistics Canada. CANSIM (database), Table 282-0004 – Labour force 2000-13 2 survey estimates (LFS), by educational attainment, gender and age group, annual Chile4 INE Chile, New National Employment Survey 2010-14 2 Estonia1 Statistics Estonia, Labour Force by county and educational level (ML123) 2000-13 3 Iceland7 Statistics Iceland Labour force survey. Educational attainment of the 2003-12 2 population 25-64 year old, 2003-12 Israel Central Bureau of Statistics Israel 2000-13 2 Japan7 Statistics Bureau, 1990, 2000 and 2010 Population Census 2000-10 2 Korea2 KOSIS, Economically Active Population Survey 2000-14 2 Mexico4 INEGI, National Population and Housing Censuses 2000-10 2 New Zealand Statistics New Zealand. Household Labour Force Survey 2000-12 2 Turkey5 TURKSTAT, Household Labour Force Survey Revised Results 2006-14 2 United States6 Census Bureau, American Community Survey (ACS), 1-year estimates, 2000-13 2 table S1501 Brazil7 IBGE, Pesquisa Nacional por Amostra de Domicílios – PNAD 2004-13 2 Colombia DANE, Great integrated household survey (GEIH for its acronym in Spanish) 2005-14 2 Russian Federation Federal State Statistics Service (Rosstat), Labour force Survey, population 2000-14 2 in age 15-72 years old

1. EU20 refers to Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Netherlands, Poland, Portugal, Slovenia, Slovak Republic, Spain, Sweden, United Kingdom (except Northern Ireland). Data refer to the labour force aged 15 and over. 2. Australia and Korea: Data refer to total labour force. 3. Canada: Data refer to the labour force aged 15 and over. 4. Chile and Mexico: Data refer to the population aged 15 and over. 5. Turkey: Illiterate people are included in the ISCED 0-2. 6. United States: Data refer to the population aged 18 and over. 7. Total labour force educational attainment includes persons not classified by level of education. 8. First year available: Slovenia and Switzerland 2001; Finland 2005; Denmark 2007. 9. Last year available: Estonia 2013.

Life expectancy at birth, total and by gender

Source Year Territorial level

EU91 Eurostat, Regional Demographic Statistics 2013 2 Australia Australian Bureau of Statistics; Table 3302.0 2013 2 Austria Statistics Austria 2013 2 Canada2 Statistics Canada; CANSIM, Table 102-0512 2011 2 Chile INE/OECD estimates 2012 2 Czech Republic Czech Statistical Office 2013 2 Denmark Statistics Denmark; Table HISBR 2013 2 Estonia Statistics Estonia; Table PO0452 2013 3 Finland Statistics Finland 2013 2 Germany Federal Office of Germany and the Statistical Offices of the Federal States 2013 2 Greece Hellenic Statistical Authority 2013 2 Hungary Hungarian Central Statistical Office 2013 2 Iceland3 n.a. - - Israel Central Bureau of Statistics 2013 2 Italy Istat; Table P.5 2013 2 Japan4 Statistics Bureau of Japan, MIC 2010 2 Korea OECD estimates based on provincial population weighted average 2013 2 Mexico5 National Institute of Statistics and Geography (INEGI) 2013 2 New Zealand6 Statistics New Zealand 2013 2 Poland Central Statistical Office of Poland 2013 2 Portugal Statistics Portugal 2013 2 Slovak Republic Statistical Office of the SR 2013 2 Spain INE 2013 2 Turkey Eurostat, Regional Demographic Statistics 2013 2 United States7 Measure of America 2010 2 Colombia DANE 2013 2 Peru National Institute of Statistics and Informatics 2013 2 Russian Federation Federal State Statistics Service 2013 2

1. EU9 refers to Belgium, France, Ireland, Netherlands, Norway, Slovenia, Sweden, Switzerland and United Kingdom (except Northern Ireland). 2. Canada: Rates used in this table for the calculation of life expectancy are calculated with data that exclude: births to mothers not resident in Canada, births to mothers resident in Canada, province or territory of residence unknown, deaths of non-residents of Canada, deaths of residents of Canada whose province or territory of residence was unknown and deaths for which age or gender of decedent was unknown. Rates used in this table for the calculation of life expectancy are based on data tabulated by place of residence. Life expectancy for the Yukon, the Northwest Territories and Nunavut should be interpreted with caution due to small underlying counts. 3. Iceland: Data not available at the regional level. 4. Japan: TL2 data computed as the average value of TL3 regions. 5. Mexico: 2011-13: CONAPO. Population forecast 2010-50, www.conapo.gob.mx. 6. New Zealand: Life expectancy data presented for each year is based on registered deaths in the three years centred on that year. New Zealand life expectancy from abridged life tables. This may differ to data from complete life tables. 7. United States: 2010 data source is Measure of America calculations using mortality counts from the Centers for Disease Control and Prevention, National Center for Health Statistics. Mortality – All County Micro-Data File, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Population counts are from the CDC WONDER Database.

Life satisfaction

Source Years Territorial level

All countries1, 2 Gallup World Poll www.gallup.com/services/170945/world-poll.aspx. Average 2006-14 TL2

1. Life satisfaction is expressed as the mean score on an 11-point scale (based on the Cantril ladder measure). It is measured using a survey question in which respondents are asked “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”. 2. Regional estimates are derived by micro-data pooling the yearly surveys 2006-14. TL3 regions for Estonia. Estimates are not available for the following regions: Northwest Territories, Nunavut, Yukon (Canada); Helsinki- Usimaa, Aland (Finland).

Local governments in metropolitan areas

Source Years Territorial level

Australia Australian Bureau of Statistics , Local Government Areas (LGA) 2011 Metropolitan areas Austria Eurostat, Gemeinden (LAU2) 2011 Metropolitan areas Belgium Eurostat, Gemeenten/Communes (LAU2) 2011 Metropolitan areas Canada Statistics Canada (Statcan), Census Subdivisions (towns, villages, etc) 2006 Metropolitan areas (CSD) Chile Instituto Nacional de Estadísticas (INE) Chile, Comunas 2002 Metropolitan areas Czech Republic Eurostat, Obce (LAU2) 2011 Metropolitan areas Denmark Eurostat, Kommuner (LAU1) 2011 Metropolitan areas Estonia Eurostat, Vald, linn (LAU2) 2011 Metropolitan areas Finland Eurostat, Kunnat/Kommuner (LAU2) 2011 Metropolitan areas France Eurostat, Communes (LAU2) 2011 Metropolitan areas Germany Eurostat, Gemeinden (LAU2) 2011 Metropolitan areas Greece Eurostat, Demotiko diamerisma/Koinotiko dimerisma (LAU2) 2011 Metropolitan areas Hungary Eurostat, Települések (LAU2) 2011 Metropolitan areas Iceland1 n.a. - - Ireland Eurostat, Local governments (LAU1) 2011 Metropolitan areas Israel1 n.a. - - Italy Eurostat, Comuni (LAU2) 2011 Metropolitan areas Japan National Land Numerical Information Service of Japan, Shi (city), Machi or 2006 Metropolitan areas Cho (town) and Mura or Son (village) Korea Korea Statistical Information Service (KOSIS), Si (city), Gun (county), 2014 Metropolitan areas Gu (district) Luxemburg EUROSTAT, Communes (LAU2) 2011 Metropolitan areas Mexico Instituto Naconal de Estadística y Geografía (INEGI), Municipios 2011 Metropolitan areas Netherlands Eurostat, Gemeenten (LAU2) 2011 Metropolitan areas New Zealand1 n.a. - - Norway Eurostat, Municipalities (LAU2) 2011 Metropolitan areas Poland Eurostat, Gminy (LAU2) 2011 Metropolitan areas Portugal Eurostat, Freguesias (LAU2) 2011 Metropolitan areas Slovak Republic Eurostat, OBCE (LAU2) 2011 Metropolitan areas Slovenia Eurostat, Obèine (LAU2) 2011 Metropolitan areas Spain Eurostat, Municipios (LAU2) 2011 Metropolitan areas Sweden Eurostat, Kommuner (LAU2) 2011 Metropolitan areas Switzerland Eurostat, Municipalities (LAU2) 2011 Metropolitan areas Turkey1 n.a. - - United Kingdom UK Office for National Statistics, Country Councils. 2001 Metropolitan areas United States U.S. Census Bureau (2002) Census of Governments, Municipalities 2000 Metropolitan areas or Townships

1. The functional urban areas, and by extension the metropolitan areas, have not been identified in Iceland, Israel, New Zealand and Turkey.

Metropolitan population: Total, by age

Source Years Territorial level

Australia Australian Bureau of Statistics 2001-11 Metropolitan areas Austria Statistics Austria 2001-11 Metropolitan areas Belgium Statistics Belgium 2001-11 Metropolitan areas Canada Statistics Canada, Census Canada 2000-11 Metropolitan areas Chile INE Chile 2002-10 Metropolitan areas Czech Republic Czech Statistical Office 2001-10 Metropolitan areas Denmark Statistics Denmark 2001-11 Metropolitan areas Estonia Statistics Estonia, Population database 2000-11 Metropolitan areas Finland Statistics Finland 2000-12 Metropolitan areas France INSEE, Demographic Census 1999-09 Metropolitan areas Germany Regionaldatenbank Deutschland 2001-10 Metropolitan areas Greece National Statistical Service of Greece 2001-11 Metropolitan areas Hungary Hungarian Central Statistical Office 2001-11 Metropolitan areas Iceland1 n.a. - - Ireland Central Statistics Office of Ireland 2002-11 Metropolitan areas Israel1 n.a. - - Italy ISTAT, Demography in Figures 2001-11 Metropolitan areas Japan Statistical Office, Population and Households data 2000-10 Metropolitan areas Korea Korea National Statistical Office 2000-10 Metropolitan areas Luxemburg STATEC – Statistical Portal 2001-12 Metropolitan areas Mexico INEGI, Demographic Census 2000-10 Metropolitan areas Netherlands Statistics Netherlands 2001-10 Metropolitan areas New Zealand1 n.a. - - Norway Statistics Norway 2001-11 Metropolitan areas Poland Central Statistical Office of Poland 2002-10 Metropolitan areas Portugal INE, Demographic Census 2001-11 Metropolitan areas Slovak Republic Statistical Office of the Slovak Republic 2001-10 Metropolitan areas Slovenia Statistical Office of the Republic of Slovenia 2002-10 Metropolitan areas Spain INE, Demographic Census 2001-10 Metropolitan areas Sweden Statistics Sweden 2000-10 Metropolitan areas Switzerland Swiss Federal Statistics Office 2000-10 Metropolitan areas Turkey1 n.a. - - United Kingdom Office for National Statistics 2001-10 Metropolitan areas United States U.S. Census Bureau 2000-10 Metropolitan areas

1. The functional urban areas, and by extension the metropolitan areas, have not been identified in Iceland, Israel, New Zealand and Turkey.

Motor vehicle theft

Source Year Territorial Level

Australia Australian Bureau of Statistics (ABS), Recorded Crime Victims, Australia, 2013 2 2013 (cat. no. 4510.0) Austria Statistics Austria, Crime Statistics 2013 2 Belgium Belgian Federal Police 2013 2 Canada1 Statistics Canada. CANSIM database, Table 252-0051 2011 2 Chile2 INE, Chile. Undersecretariat of Crime Prevention, Ministry of Interior 2013 2 and Public Safety Czech Republic Czech Statistical Office CZSO, Police of the Czech Republic 2013 2 Germany8 n.a. - - Denmark Statistics Denmark, StatBank Table STRAF11 2013 2 Estonia8 n.a. - - Finland Statistics Finland, Justice statistics 2013 2 France3 INSEE, Etat 4001 annuel, DCPJ 2012 2 Greece8 n.a. - - Hungary OECD Regional Questionnaire; information provided by the delegate 2013 2 of the Working Party of Territorial Indicators (WPTI) Ireland CSO, StatBank Ireland. Table CJQ02 2011 2 Iceland8 n.a. - - Israel Central Bureau of Statistics Israel 2013 2 Italy National Statistical Institute, ISTAT 2013 2 Japan National Police Agency. Publications of the Police Policy Research Center: 2014 2 Crime in Japan in 2014 Korea8 n.a. - - Luxembourg8 n.a. - - Mexico4 National Statistical Institute, INEGI 2011 2 New Zealand5 New Zealand Police 2014 2 Netherlands8 n.a. - - Norway8 n.a. - - Poland National Police Headquarters 2011 2 Portugal Ministry of Justice of Portugal – Directorate-General for Justice Policy, 2013 2 motor vehicle theft crimes recorded by the police Slovak Republic6 Statistical Office of the Slovak Republic, regional database 2013 2 Slovenia OECD Regional Questionnaire; information provided by the delegate 2012 2 of the Working Party of Territorial Indicators (WPTI) Spain INE 2013 2 Sweden Swedish National Council for Crime Prevention (Brå) 2014 2 Switzerland7 Federal Statistical Office (FSO). Police crime statistics 2013 2 Turkey General Directorate of Security, General Commandership of Gendarme 2013 - United Kingdom8 n.a. - - United States Federal Bureau of Investigation, Crime in the United States. Table 4, 2013 2 by Region, Geographic Division and State

1. Canada: total theft of motor vehicle, actual incidents. 2. Chile: data based on crimes known by police (called “casos policiales” in Spanish). Do not include motor attempted theft of vehicles. 3. France: data includes car theft (index 35), theft of motor vehicles with two wheels (index 36) and theft of vehicles with cargo (index 34). Some motor vehicle thefts are recorded by the corresponding national institutions (such as central offices) of the police and gendarmerie. These thefts are not registered in a particular TL3 region, thus the national total does not fully correspond with the sum of the TL3 regions. 4. Mexico: National Census 2012 State Law Enforcement. As part of the implementation of the National Census of Law Enforcement 2011 and 2012, the figure provided for 2010 and 2011 corresponds to the data of the relevant offenses, registered preliminary inquiries initiated by the Public Prosecutor of the Common Jurisdiction in each of the federal states. 5. New Zealand: the number of offences police recorded for theft or unlawful taking of a motor vehicle. This includes instances where a vehicle is taken for a joy ride and later recovered, as well as instances where vehicles are taken permanently. 6. Slovak Republic: since 2005, data on NUTS 1 level need not to be equal to the sum of NUTS 2 level data because NUTS 1 data also includes regionally unspecified offences recorded by Railway Police, Military Police, Corps of Prison and Court Guard, and Customs Director. 7. Switzerland: from 2009, police statistics on crime have been revised and are thus not comparable to the old police statistics; this translates into a break in series between 2008 and 2009. 8. Germany, Estonia, Greece, Iceland, Korea, Luxembourg, Netherlands, Norway, and United Kingdom: data not available at the regional level.

Municipal waste and recycled waste

Source Years Territorial level

All countries1, 2, 3 OECD Regional Database 1995-13 2 Regional municipal data were provided by the individual member countries through the annual OECD regional data questionnaire

National data: OECD (2015), Environment at a Glance 2015: OECD Indicators, OECD Publishing, Paris, http://dx.doi.org/ 10.1787/9789264235199-en. 1. No municipal waste data at regional level are available for Australia, Denmark, Finland, Germany, Greece, Iceland, New Zealand, Sweden, Switzerland and the United States. Last year available: Canada (2008), Mexico (2009), Ireland (2010), France and the United Kingdom (2011), Chile, Spain and Turkey 2012. 2. No municipal waste recycling data at regional level are available for Australia, Canada, Chile, Denmark, Finland, Greece, Ireland, Israel, Iceland, Mexico, New Zealand, Spain, Sweden, Switzerland, Turkey and United States. Last year available: France and United Kingdom (2011), Germany 2012. 3. National data: last available year: Australia and Chile 2009, Japan 2010, Austria, Greece, Ireland, Korea, Mexico and United States 2012. First year available: Australia and Israel 2000.

Number of rooms per person

Source Year Territorial level

Australia Australia Bureau Statisitics, table 4130.0 2011 2 Austria Statistics Austria, Microcensus Housing Survey 2013 2 Belgium Eurostat, Regional Statistics 2012 2 Canada Statistics Canada 2011 2 Chile n.a. - - Czech Republic Czech Statistical Office, EU SILC 2013 2 Denmark OECD Regional Questionnaire/information provided by the delegate of the 2014 2 Working Party on Territorial Indicators Finland Statistics Finland, 2012 2 France Insee, Population census 2010 2 Germany Eurostat, Regional Statistics 2013 2 Greece Hellenic Statistical Authority, Population – Housing Census 2013 NUTS 1 Hungary Hungarian Central Statistical Office, Population micro-census 2011 2 Iceland n.a. - - Ireland Eurostat, Regional Statistics 2012 2 Israel Central Bureau of Statistics Israel 2013 2 Italy ISTAT, Population and housing Census 2011 2 Japan Statistics Bureau of Japan 2013 2 Korea Statistics Korea, Housing Census General 2010 2 Mexico National Institute of Statistics and Geography (INEGI) 2010 2 Netherlands Eurostat, Regional Statistics 2012 2 New Zealand Statistics New Zealand 2013 2 Norway Eurostat, Regional Statistics 2012 2 Poland OECD estimates based on Central Statistical Office – dwelling stock by 2012 2 location Portugal Statistics Portugal, Population and housing census 2011 2 Slovak Republic Statistical Office of the SR, Household Budget Survey 2013 2 Slovenia Eurostat, Regional Statistics 2013 2 Spain INE 2012 2 Sweden Eurostat, Regional Statistics 2012 2 Switzerland Federal Statistical Office, GWS 2013 2 Turkey Information provided by the delegate of the Working Party on Territorial 2012 2 Indicators United Kingdom1 Eurostat, Regional Statistics 2011 2 United States American Community Survey 2012 2

1. United Kingdom: Regional values available except for Scotland.

Part-time employment total and by gender

Source Year Territorial level

EU23 countries1 Eurostat, Employment by full-time/part-time, table lfst_r_lfe2eftpt 2014 2 Australia Australian Bureau of Statistics (ABS), Labour Force, (cat. no. 6291.55.001): 2014 2 EM3 – Employed Person by Usual Hours Worked Canada2, 6 Statistics Canada. CANSIM database, Table 282-0002 – Labour force survey 2014 2 estimates (LFS) Chile OECD Regional Questionnaire; information provided by the delegate 2014 2 of the Working Party of Territorial Indicators (WPTI) Iceland n.a. - - Israel3 Central Bureau of Statistics Israel 2013 2 Japan OECD Regional Questionnaire; information provided by the delegate 2014 2 of the Working Party of Territorial Indicators (WPTI) Korea n.a. - - Mexico INEGI. Encuesta Nacional de Ocupación y Empleo (ENOE) 2014 2 New Zealand Statistics New Zealand 2012 2 Norway Statistics Norway 2013 2 Switzerland Eurostat, Employment by full-time/part-time (lfst_r_lfe2eftpt) 2014 2 Turkey4 TURKSTAT, Household Labour Force Survey Revised Results 2014 2 United States5 U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics 2013 2 program. Current Population Survey, Geographic Profile of Employment and Unemployment, table 22 Brazil IBGE, Pesquisa Nacional por Amostra de Domicílios – PNAD 2013 2 Colombia DANE – Gran Encuesta Integrada de Hogares – GEIH – (Labour Houshold 2012 2 survey) Russian Federation Federal State Statistics Service (Rosstat), Labour force Survey, population 2014 2 in age 15-72 years old

The definition of part-time work varies considerably across OECD member countries. The OECD defines part-time working in terms of usual working hours fewer than 30 per week. At regional level there does not exist a harmonised definition of part-time employment. Indeed, for some countries, the number of hours defining the number of part- time employees in a region differs from the OECD definition. This makes regional values to differ from national estimates relying on a harmonised definition. However, for European TL2 regions, the distinction between full-time and part-time work is based on a spontaneous response by the respondent; except in the Netherlands, Iceland and Norway were part-time is determined if the usual hours are fewer than 35 hours. 1. EU23 countries: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden and United Kingdom. 2. Canada: Part-time employment consists of persons who usually work less than 30 hours per week at their main or only job. Estimates in thousands, rounded to the nearest hundred. 3. Israel: part-time employment consists of persons who usually work less than 35 hours a week. 4. Turkey: Total figures may not be exact due to the rounding of the numbers. Sample size is too small for reliable estimates for figures less than two thousand persons in each cell. Full time/part time distinction is made by the usual hours worked in the main job using 30 hours threshold. 5. United States: a part-time schedule in the U.S. is officially defined as 1–34 hours per week. To approximate the OECD definition of less than 30 hours per week, the first two categories from the persons at work by hours of work table were added up. Hence, the universe for the data below excludes persons who were not at work during the Current Population Survey reference week. (Nationally in 2013, about 3.5 per cent of employed persons were not at work in an average week). 6. Canada and Finland: no data on part time employment by gender.

Perception of corruption

Country Source Years Territorial level

All countries1, 2, 3 Gallup World Poll www.gallup.com/services/170945/world-poll.aspx. Average 2006-14 TL2

1. Perception of corruption is measured using a survey question in which respondents are asked to rank from 0 to 10: “Is corruption widespread throughout the government in (this country), or not?”. 2. Regional estimates are derived by micro-data pooling the yearly surveys 2006-14. Estimates are TL2 except for New Zealand for which data is available only for North Island and South Island and TL3 regions for Estonia. 3. Further details in Brezzi, M. and M. Díaz Ramírez (2016),“Building subjective well-being indicators at the subnational level: A preliminary assessment in OECD regions”, OECD Regional Development Working Papers, No. 2016/03, OECD Publishing, Paris, http://dx.doi.org/10.1787/5jm2hhcjftvh-en.

PCT patents applications

Source Years Territorial level

All countries OECD REGPAT Database 1995-2013 2 and 3 OECD 191, 2, 3, 4 OECD REGPAT Database 2012 Metropolitan areas

1. The OECD REGPAT Database presents patent data that have been linked to regions according to the addresses of the applicants and inventors. For more information on the database, see: www.oecd.org/dataoecd/22/19/ 40794372.pdf. 2. A patent is generally granted by a national patent office or by a regional office that does the work for a number of countries, such as the European Patent Office and the African Regional Intellectual Property Organization. Under such regional systems, an applicant requests protection for the invention in one or more countries, and each country decides whether to offer patent protection within its borders. In this publication the patent data comes from the WIPO-administered Patent Co-operation Treaty (PCT) which provides for the filing of a single international patent application which has the same effect as national applications filed in the designated countries. An applicant seeking protection may file one application and request protection in as many signatory states as needed. More info on PCT: www.wipo.int/export/sites/www/pct/en/basic_facts/faqs_about_the_pct.pdf. 3. Patent counts are provided for selected technology areas such as information and communication technology (ICT), biotechnology, nanotechnology and for technologies related to the environment and health. For more information, see www.oecd.org/dataoecd/5/19/37569377.pdf. For classifications of environmental related technologies see www.oecd.org/env/consumption-innovation/indicator.htm. 4. OECD (19) refers to Australia, Austria, Belgium, Canada, Chile, Denmark, Estonia, Finland, France, Germany, Italy, Japan, Mexico, Netherlands, Norway, Portugal, Spain, Sweden, and the United States. Only for these 19 countries was it possible to link the addresses of the applicants and inventors to the post codes of municipalities belonging to the metropolitan area.

Physicians

Source Years Territorial Level

EU23 countries1 Eurostat, health personnel by NUTS 2 regions (hlth_rs_prsrg) 2013 2 Australia2 Australian Institute of Health and Welfare (AIHW), Medical Workforce 2012 2012 2 Canada3 Canadian Institute of Health Information (CIHI) Canadian Insitute for 2011 2 National Health Information (CIHI). Physician Database, table A.1.5 Chile Department of Health Statistics and Information (DEIS), Ministry of 2011 2 Health (Minsal) Iceland n.a. - - Israel Central Bureau of Statistic (CBS) 2012 2 Japan Statistics and Information Department, Minister's Secretariat, Ministry 2012 2 of Health, Labour and Welfare Korea Korea National Statistical Office 2013 2 Mexico Ministry of Health 2013 2 New Zealand Medical Council, The New Zealand Medical Force in 2010 2010 2 Norway Eurostat, Regional health statistics 2013 2 Switzerland FSO Federal Statistical Office, Neuchâtel; Swiss Medical Association (FMH), 2013 2 Bern; Medical Statistics of Physicians, yearly census Turkey National Statistics Agency, TURKSTAT 2013 2 United States4 American Medical Association 2011 2 China5 National Bureau of Statistics China 2013 2 Peru Ministerio de Salud-Oficina de Estadística e Informática-Registro Nacional 2012 2 de Establecimientos de Salud Russian Federation Federal State Statistics Service (Rosstat) 2013 2

PM2.5 particles concentration Source Years Territorial level

All countries van Donkelaar, A., et al. (2014) “Use 2012-14 2 and metropolitan of Satellite Observations for Long-Term Exposure Assessment of Global areas Concentrations of Fine Particulate Matter”, Environmental Health Perspectives, Vol. 123(2).

The methodology is described in the Annex C.

Population: Total, by age and gender

Source Years Territorial Level

Australia Australian Bureau of Statistics, cat. no. 3235.0, Population Estimates by Age 2001-14 3 and Sex, Regions of Australia (ASGS 2011), population at 30 June Austria Statistics Austria, Population statistics, population at 1 January 2000-14 3 Belgium Federal Public Service (FPS) Economy/Statistics Belgium. Official count 2000-14 3 of the resident population, population at 1 January Canada Statistics Canada. CansimTable 051-0062. Population Estimates based 2000-14 3 on Standard Geographical Classification 2011, population at 1 July Chile INE, Chile. Population projection and estimates by sex and age. 1990-2020, 2000-14 3 average annual population Czech republic1 Czech Statistical Office CZSO. Population of territorial units of the 2000-14 3 Czech Republic Denmark Statistics Denmark – StatBank, (FOLK1), population at 1 January 2008-14 3 Estonia Statistics Estonia. Statistical database – table PO022, population 2000-14 3 at 1 January Finland Statistics Finland, Population Statistics, Population structure, population 2000-14 3 at 1 January France Insee – Estimations de population pour la France métropolitaine, population 2000-14 3 at 1 January Germany Spatial Monitoring System of the Federal Institute for Building (BBSR). 2000-14 3 Statistical Offices of the Federal States, table 173-21-5-B Greece Hellenic Statistical Authority, Population statistics, population at 1 January 2001-14 3 Hungary HCSO, Hungarian Central Statistical Office, population at 1 January 2000-14 3 Iceland Statistics Iceland, population at 1st of January by municipality 2000-14 3 Ireland CSO, StatBank Ireland, population estimates: table PEA07, population 2000-14 3 in April; 2014 data collected from Eurostat Israel1, 3 Central Bureau of Statistics Israel 2000-14 2 Italy National Institute for Statistics (Istat). Intercensal resident population 2000-14 3 estimates (1991-2001 and 2002-2010) and population projection for reference year 2011 onwards. Population at 1 January; 2014 data collected from Eurostat Japan Statistics Bureau, Current Population Estimates as of 1 October 2001-14 3 Korea Statistics Korea, KOSIS database, yearly average projected population 2001-14 3 by age, population at 1 October Luxembourg Eurostat regional statistics, table demo_r_pjangrp3, population at 1 January 2000-14 3 Mexico INEGI, mid-year estimates, Population and Housing Census 2000-10 3 (1990,95,00,05,2010), OECD estimates for inter-census years. As from 2011 data are based on population projection, population at 30 June Netherlands Eurostat regional statistics, table demo_r_pjangrp3, population at 1 January 2003-14 3 New Zealand Statistics New Zealand, Population Statistics. Boundaries at 1 January 2013. 2000-14 3 NZ.DOTSTAT (Tablecode 7501), population at 30 June Norway Statistics Norway, population at 1 January; 2014 data collected from 2000-14 3 Eurostat Poland1 Central Statistical Office of Poland. Local Data Bank (Population and Vital 2000-14 3 statistics – Population by sex and age group (NTS-5) Portugal Statistics Portugal (INE), Demographic Statistics, population at 1 January 2000-14 3 Slovak Republic Statistical Office of the Slovak Republic, population at 1 January 2000-14 3 Slovenia Statistical Office of the Republic of Slovenia. SI-STAT Data Portal. population 2000-14 3 at 1 January; 2014 data collected from Eurostat Spain INE-INEBASE Population data historical series, 1971 to 2014, population 2000-14 3 at 1 January

Population: Total, by age and gender (cont.)

Source Years Territorial Level

Sweden1 Statistics Sweden 2000-14 3 Switzerland1 Swiss Federal Statistical Office: from Dec-2010 onwards (Population 2000-14 3 and Households Statistics (STATPOP) ; Dec-1990 to Dec-2009: Annual Population Statistics (ESPOP); break in series between 2010 and 2011 Turkey1, 3 Turkish Statistical Institue (TurkStat). The source of 2007-14 data is Address 2001-13 3 Based Population Registration System (ABPRS) and de jure population United Kingdom Office for National Statistics, ONS, Nomis database, population at 1 January 2000-14 3 for TL2; 2014 data collected from Eurostat United States United States Census Bureau – State and County Population Estimates, 2000-14 3 Table PEPAGESEX, population at 1 July Brazil2 Instituto Brasileiro de Geografia e Estatística, IBGE, 2004-14 2 census 1991, 2000, 2010 China China Statistical database – Age composition and dependency ratio 2000-14 2 of population table Colombia DANE. Estimation of population 1985-2005 and projection of population 2000-14 2 2005-2020 by department India Estimated mid-year population by states/UTs 2001-13 2 Indonesia Statistics Indonesia – Population of Indonesia by Province 2000-14 2 Latvia Central Statistical Bureau of Latvia, population by statistical region 2000-14 3 Lithuania Eurostat regional statistics, population on 1 January, table demo_r_pjangrp3 2000-14 3 Russian Federation Federal State Statistics Service (Rosstat). Number of de-jure (resident) 2000-14 2 population on subjects of the Russian Federation South Africa Statistics South Africa, population estimates for the period 2002-2014 based 2002-14 2 on 2011 Census 1. Population at 31 December restated at 1 January the following year by OECD. 2. First available year for population by age: 2004. 3. Last available year for population by age: 2013.

Population mobility among regions (total and young)

Source Years Territorial level

Australia1 Australian Bureau of Statistics (ABS), ABS.Stat 2011-13 3 Austria Statistics Austria, Migration statistics 2011-13 3 Belgium FPS Economie/Statistics Belgium 2011-13 3 Canada Statistics Canada. Cansim Table 051-0012 2011-13 2 Chile6 n.a. - - Czech Republic Czech Statistical Office CZSO 2011-13 3 Denmark Statistics Denmark, StatBank, table FLY55 2011-13 3 Estonia Statistics Estonia, Statistical database, table POR06 2011-13 3 Finland Statistics Finland, Population Statistics, Migration 2011-13 3 France6 n.a. - - Germany7 Spatial Monitoring System of the BBSR. Periodic update of population 2011-12 3 statistics by the Federal Office of Germany and the Statistical Offices of the Federal States Greece Hellenic Statistical Authority. Population-Housing Census (2001, 2011) 2011 3 Hungary HCSO, Hungarian Central Statistical Office, Internal migration statistics 2011-13 3 based on the registration system of home addresses Iceland Statistics Iceland, Internal migration 2011-13 3 Ireland6 n.a. - - Israel Central Bureau of Statistics Israel 2011-13 2 Italy Istat, Iscrizioni e calcellazioni anagrafiche (changes of residence from/to 2011-13 3 italian municipalities) Japan7 Statistics Bureau, Migrants by prefecture derived from the Basic Resident 2011-13 3 Registers Korea2 Statistics Korea, KOSIS database – Internal Migration Statistics 2011-13 3 Mexico INEGI. Censo de población y vivienda 2010 2010 3 Netherlands Statistics Netherlands on Statline 2010 2 New Zealand6 n.a. - -

Population mobility among regions (total and young) (cont.)

Source Years Territorial level

Norway Statistics Norway. Statbank, table 01222: Population change (M) 2011-13 3 Poland Central Statistical Office of Poland, PESEL register 2011-13 3 Portugal3 Statistics Portugal (INE), Census 2001 and 2011 2011 3 Slovak Republic Statistical Office of the SR 2011-13 3 Slovenia Statistical Office of the Republic of Slovenia, Ministry of the Interior – 2011 3 Central Population Register, Ministry of the Interior – Administrative Internal Affairs Directorate Spain INE – Data provided by the delegate of the OECD Working Party on Territorial 2011-13 3 Indicators Sweden Statistics Sweden, Central Office for Administrative and Electronic Public 2011-13 3 Services registration system Switzerland Swiss Federal Statistical Office, 1990 to 2010: Annual Population Statistics 2011-13 3 (ESPOP), from 2011 onwards: Population and Households Statistics (STATPOP) Turkey Turkish Statistical Institue (TurkStat), Address Based Population 2011-13 3 Registration System United Kingdom4 National Statistical Office, Population Estimates 2011-13 3 United States5 Secretariat's calculation using Internal Revenue Service (IRS) Individual 2011 3 Master File, Statistics of Income. Brazil IBGE, 1991, 2000 e 2010 Census, 2004-13: Pesquisa Nacional por Amostra 2011-13 2 de Domicílios – PNAD Russian Federation Federal State Statistics Service (Rosstat) calculations based on Federal 2011-13 2 Migration Service data Data refer to domestic migration: inflows and outflows of population from one region to another region of the same country. They do not include international immigration and outmigration. 1. Australia: Regional internal migration covers the movement of people from one location to another within Australia. Regional internal migration estimates (RIME) are prepared for sub-state regions and captures moves over each financial year on an annual basis. 2. Korea: Sejong Province, new province created as from August 2012. Due to limited data availability, Sejong data have been aggregated in Chungcheongnam-do (KR053). 3. Portugal: 2011 census micro-data refer to flows between 31 December 2009 and 21 March 2011. 4. United Kingdom: data do not include Scotland and Northern Ireland. 5. United States: Secretariat’s computation of inflows and outflows at TL3 level by aggregating county-to-county bilateral migration data from the IRS Individual Master File system, based on tax filing units. www.irs.gov/uac/SOI- Tax-Stats-County-to-County-Migration-Data-Files. 6. France and Ireland data not available at regional level. Chile and New Zealand regional data are not included for lack of comparability with the other countries. 7. Young immigrants data available for the period 2009-12 for Germany and 2010-13 for Japan.

Research and development (R&D) expenditure

Source Years Territorial level

EU211 Eurostat, Regional Science and technology Statistics, R&D expenditures 2001-13 2 and personnel, Total intramural R&D expenditure (GERD) by sector of performance and region Australia2 Australian Bureau of Statistics 2000-12 2 8104.0 – Research and Experimental Development, Businesses, Australia, 2010-11 8109.0 – Research and Experimental Development, Government and Private Non-Profit Organisations, Australia, 2008-09 8111.0 – Research and Experimental Development, Higher Education Organisations, Australia, 2010 Canada Statistics Canada. CANSIM database, 2000-13 2 Table 358-0001 – Gross domestic expenditures on research and development, by performer sector Chile Instituto Nacional de Estadísticas (INE) Chile, 2009-12 2 Survey of Expenditure and Personnel in R&D Iceland n.a. - - Israel Central Bureau of Statistics 2007-08 2 Japan n.a. - - Korea Korea Institute of Science and Technology Evaluation and Planning (KISTEP) 2000-13 2 Mexico n.a. - - New Zealand n.a. - - Norway Eurostat, Regional Science and Technology Statistics, R&D expenditures 2001-13 2 and personnel, Total intramural R&D expenditure (GERD) by sector of performance and region Switzerland3 Eurostat, Regional Science and Technology Statistics, R&D expenditures 2008-12 2 and personnel, Total intramural R&D expenditure (GERD) by sector of performance and region Turkey n.a. - - United States National Science Foundation, National Center for Science and Engineering 2000-13 2 Statistics. Science and Engineering State Profiles www.nsf.gov/statistics/ states/#ui-tabs-4.

Gross Domestic Expenditure on R&D (GERD) is the total intramural expenditure on R&D performed in the region or country during a given period. GERD is disaggregated in four sectors: business enterprise, government, higher education and private and non-profit. The Business Enterprise sector is comprehensive of all firms, organizations and institutions whose primary activity is the market production of goods or services (other than higher education) for sale to the general public at an economically significant price. It also includes the private non-profit institutions mainly serving the above mentioned firms, organizations and institutions (See Frascati Manual section 3.4). The Government sector is comprehensive of all departments, offices and other bodies which furnish, but normally do not sell to the community, those common services, other than higher education, which cannot otherwise be conveniently and economically provided, as well as those that administer the state and the economic and social policy of the community. (Public enterprises are included in the business enterprise sector). It also includes non- profit institutions controlled and mainly financed by government, but not administered by the higher education sector (see Frascati Manual section 3.5). The higher education sector is comprehensive of all universities, colleges of technology and other institutions of post-secondary education, whatever their source of finance or legal status. It also includes all research institutes, experimental stations and clinics operating under the direct control of or administered by or associated with higher education institutions (see Frascati Manual section 3.7). The Private non- profit sector is comprehensive of Non-market, private non-profit institutions serving households (i.e. the general public) and private individuals or households (see Frascati Manual section 3.6). Source: OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris, http:// dx.doi.org/10.1787/9789264239012-en. First available year: 2001 for Czech Republic and Sweden; 2002 for Austria, Belgium and Ireland; 2003 for Germany and Slovenia, 2005 for Netherlands and United Kingdom; 2007 for Denmark. Only 2011 data for Greece. 1. EU21 countries: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Netherlands, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, and the United Kingdom. 2. Australia: 2012 R&D Business expenditures for Australia refer to 2013-14 fiscal year. 3. Switzerland: only Business R&D expenditure.

Research and development (R&D) personnel (headcounts)

Source Years Territorial Level

EU211 Eurostat, Total R&D personnel by sectors of performance (employment) 2001-13 2 and region Australia3 n.a. - - Canada2 Statistics Canada. CANSIM database Table 358-0160 Provincial distribution 2013 2 of personnel engaged in research and development, by performing sector and occupational category Chile Instituto Nacional de Estadísticas (INE) Chile, Survey of Expenditure 2009-12 2 and Personnel in R&D Iceland3 n.a. - - Israel Central Bureau of Statistics 2007-08 2 Japan3 n.a. - - Korea Korea Institute of Science and Technology Evaluation and Planning (KISTEP) 2000-13 2 Mexico3 n.a. - - New Zealand3 n.a. - - Norway Eurostat, Total R&D personnel by sectors of performance (employment) 2001-13 2 and region Switzerland5 Eurostat, Total R&D personnel by sectors of performance (employment) 2008-12 2 and region Turkey3 n.a. - - United States4 National Science Foundation, National Center for Science and Engineering 2008-13 2 Statistics; Science and Engineering State Profiles www.nsf.gov/statistics/ states/#ui-tabs-4

1. EU21: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Netherlands, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, and United Kingdom. R&D personnel by sectors for France: 2001. First available year: 2001 for Czech Republic, Greece and Sweden; 2002 for Austria, Belgium and Ireland; 2003 for Germany and Slovenia; 2005 for Netherlands and the United Kingdom; 2007 for Denmark; 2009 for Finland. 2. Canada: Data are expressed in full-time equivalent. 3. Australia, Iceland, Mexico, New Zealand: Data not available at the regional level. Data available for Israel are Higher Education and Business R&D personnel, and for Japan, total Government R&D personal. 4. United States: total R&D personnel estimate: based on employed science, engineering, or health (SEH) doctorate holders. 5. Switzerland: only Business R&D personnel.

Social network support

Source Years Territorial level

All countries Gallup World Poll www.gallup.com/services/170945/world-poll.aspx Average 2006-14 TL2

Perceived social network support is based on the survey question: “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?” Regional estimates are derived by micro-data pooling the yearly surveys 2006-14 and they show the percentage of the regional sample responding “Yes” to the survey question. TL3 regions for Estonia. Estimates are not available for the following regions: Northwest Territories, Nunavut, Yukon (Canada); Helsinki-Usimaa, Aland (Finland).

Subnational government expenditure, revenue, investment and debt

Source Years Territorial level

All countries1, 2, 3, 4, 5 OECD National Accounts 2014 -

Tax revenue: comprises taxes on production and imports (D2), current taxes on income and wealth (D5) and capital taxes (D91). It includes both own-source tax revenue (or “autonomous”) and tax revenue shared between central and subnational governments. NB: the SNA 2008 has introduced some changes concerning the classification of some shared tax revenues. In several countries, certain tax receipts have been recently reclassified as transfers and no longer as shared taxes. 1. Data at country level are derived mainly from the OECD National Accounts harmonised according to the new standards of the System of National Accounts (SNA) 2008, implemented by most OECD countries since December 2014 (exceptions are, at the time of this publication: Chile, Japan and Turkey which are still under SNA 1993). They are complemented by data from Eurostat, IMF (Chile) and national statistical institutes for some countries or indicators (in particular, territorial organisation). Data were extracted in January 2016. Subnational government: is defined here as the sum (non-consolidated) of subsectors S 1312 (federated government) and S 1313 (local government). 2. Total public expenditure comprises: current expenditure (compensation of employees, intermediate consumption, social expenditure, subsidies and other current transfers, taxes, financial charges, adjustments) and capital expenditure (investments plus capital transfers (i.e. investment grants and subsidies in cash or in kind made by subnational governments to other institutional units). 3. Total public revenue comprises tax revenue (see below), transfers (current and capital grants and subsidies), tariffs and fees, property income and social contributions; 4. Public investment includes gross capital formation and acquisitions, less disposals of non-financial non- produced assets. Gross fixed capital formation (or fixed investment) is the main component of investments. NB: since the new standards of the SNA 2008, expenditures on research and development and weapons systems are included in gross fixed capital formation. 5. The General Government gross debt definition based on the SNA 2008, includes the sum of the following liabilities: currency and deposits + debt securities + loans + Insurance pension and standardised guarantees + other accounts payable. Most debt instruments are valued at market prices. NB: OECD definition differs from the one defined in the EU Maastricht protocol which is restricted to the sum of the first three items (i.e. mainly borrowing).

Voter turnout

Source Last Year Territorial level

Australia Australian Electoral Commission. Federal election 2013 2 Austria Austrian Federal ministry of interior, parliamentary elections 2013 2 Belgium Federal Portal of Belgium. Parliamentary elections 2013 2 Canada Elections Canada, Election Results 19 October 2015 – enr.elections.ca 2015 2 Chile INE, Chile. Electoral service (Servel) 2013 2 Czech Republic Czech Statistical Office CZSO, Results of Election to the Chamber of Deputies 2013 2 of the parliament Denmark Danish general election – http://electionresources.org/dk/data/ 2015 2 Estonia Estonian parliamentary election – http://rk2015.vvk.ee/detailed.html 2015 3 Finland Statistics Finland, Presidential elections, second round 2012 2 France BEEP – Ministère de l'intérieur 2012 2 Germany Data sent by the German delegate of the OECD Working Party on Territorial 2013 2 Indicators, German Federal election Greece Ministry of Interior, Parliamentary Elections 2012 – www.ypes.gr/en/ 2012 2 Elections/ Hungary Hungarian National Election Office 2014 2 Iceland Results of general elections – www.statice.is/statistics/population/elections/ 2003 2 general-elections/ Ireland Houses of the Oireachtas – www.oireachtas.ie 2011 2 Israel Central Bureau of Statistics Israel 2013 2 Italy Ministero dell'interno, Dipartimento per gli Affari Interni e Territoriali. Servizi 2013 2 Elettorali Japan Statistics Bureau (2014: Representatives elections) 2014 2 Korea Korean National Election Commission 2014 2 Mexico INEGI, general elections 2012 2 Netherlands Dutch Electoral Council (Kiesraad) – www.kiesraad.nl/ 2012 2 New Zealand New Zealand Electoral Commission, general election 2014 2 Norway Statistics Norway 2013 2 Poland Central Statistical Office of Poland, National Election Commission 2015 2

Voter turnout (cont.)

Source Last Year Territorial level

Portugal Ministry of Internal Administration of Portugal- Directorate-General 2015 2 of Internal Administration Slovak Republic Statistical Office of the SR 2014 2 Slovenia Republic of Slovenia Early elections for deputies to the National Assembly 2014 2 Spain INE 2015 2 Sweden Swedish Election Authority 2014 2 Switzerland Statistique suisse – www.politik-stat.ch/nrw2015wb_fr.html 2015 2 Turkey Data sent by the Turkish delegate of the OECD Working Party on Territorial 2011 2 Indicators United Kingdom Data sent by the UK delegate of the OECD Working Party on Territorial 2015 2 Indicators United States US Census. Reported Voting and Registration of the Citizen Voting-Age 2012 2 Population

Young people neither in employment nor in education or training (NEET)

Reference Source Year Territorial level population

EU211 Eurostat, Young people neither in employment nor in education 15-24 2014 2 and training by sex and NUTS 2 regions (NEET rates) [edat_lfse_22] Australia n.a. - - - Canada n.a. - - - Chile n.a. - - - Iceland n.a. - - - Israel Central Bureau of Statistics Israel 15-24 2013 2 Japan Statistics Bureau 15-24 2014 2 Korea n.a. - - - Mexico n.a. - - - New Zealand Statistics New Zealand. Infoshare database, Household Labour 15-24 2014 2 Force Survey Norway Eurostat, Young people neither in employment nor in education 15-24 2014 2 and training by sex and NUTS 2 regions (NEET rates) [edat_lfse_22] Switzerland Eurostat, Young people neither in employment nor in education 15-24 2014 2 and training by sex and NUTS 2 regions (NEET rates) [edat_lfse_22] Turkey TURKSTAT, Household Labour Force Survey Revised Results 15-24 2014 2 United States n.a. - - - Brazil IBGE, Pesquisa Nacional por Amostra de Domicílios – PNAD 15-24 2013 2 Colombia DANE 15-24 2014 2 Russian Federation Federal State Statistics Service (Rosstat), Labour force survey 15-24 2014 2 South Africa Statistics South Africa, General Household Survey 2002-13 15-24 2013 2

The indicator on young people neither in employment nor in education and training (NEET) corresponds to the percentage of the population 18-24 who are not employed and not involved in further education or training. The numerator of the indicator refers to persons who meet the following two conditions: (a) they are not employed (i.e. unemployed or inactive according to the International Labour Organisation definition) and (b) they have not received any education or training in the four weeks preceding the survey. The denominator in the total population consists of the same age group and gender, excluding the respondents who have not answered the question “participation to regular education and training”, http://ec.europa.eu/eurostat/data/database. 1. EU21 countries: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, Netherlands, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden and United Kingdom.

Youth unemployment

Reference Source Year Territorial level population

EU231 Eurostat, Regional labour market statistics, unemployment 15-24 2008-14 2 Australia Australian Bureau of Statistics (ABS), 15-24 2008-14 2 Labour Force, Cat. no. 6291.0.55.001 Canada2 Statistics Canada. CANSIM database, Table 282-0002 – Labour 15-24 2008-14 2 force survey estimates (LFS) Chile National Institute of Statistics, INE 15-24 2010-14 2 Iceland n.a. 15-24 2008-11 2 Israel Central Bureau of Statistics – LFS 15-24 2008-13 2 Japan Statistics Bureau, MIC 15-24 2008-14 2 Korea n.a. - - - Mexico National Institute of Statistics, INEGI, Employment and Occupation 15-24 2010-14 2 National Survey New Zealand Statistics New Zealand – Household Labour Force Survey 15-24 2008-12 North/South Islands Norway Statistics Norway 15-24 2008-14 2 Switzerland Eurostat, Regional labour market statistics, unemployment 15-24 2009-14 2 Turkey Turkish Statistical Institute, LFS 15-24 2008-14 2 United States Bureau of Labour Statistics, Local Area Unemployment Statistics 15-24 2008-14 2 Brazil IBGE, Pesquisa Nacional por Amostra de Domicílios – PNAD 15-24 2008-13 2 Colombia DANE – Gran Encuesta Integrada de Hogares – GEIH – (Labour 15-24 2008-14 2 Houshold survey) Russian Federation Federal State Statistics Service (Rosstat), Labour force survey 15-24 2008-14 2

ANNEX C

Indexes and estimation techniques

Gini index Definition: Regional disparities are measured by an unweighted Gini index. The index is defined as: 2 N 1 GINI = i QF i N 1 i 1 i

y j i j 1 where N is the number of regions,Fi , Qi and yi is the value of variable y N n yi i 1 (e.g. GDP per capita, unemployment rate, etc.) in region j when ranked from low (y1)to high (yN) among all regions within a country.

The index ranges between 0 (perfect equality: y is the same in all regions) and 1 (perfect inequality: y is nil in all regions except one). Interpretation: The index assigns equal weight to each region regardless of its size; therefore differences in the values of the index among countries may be partially due to differences in the average size of regions in each country. Only countries with more than four regions are included in the computation of the Gini index.

Malmquist decomposition Definition: The Malmquist index allows the decomposition of the productivity growth of a region between two effects, the frontier shift effect which is the change of regional productivity related to the gain of productivity of the frontier, and the catch-up effect which is the acceleration of the productivity of the region towards the frontier. The frontier in this publication is defined, by country, as the top 10% regions with the highest GDP per employee until the equivalent of 10% of national employment is reached. The frontier at OECD level is the simple average of each country’s frontier.

Productivity growth = frontier shift-effect × catch-up effect

Output Output ( GDP) ( GDP) Frontier (t+1) Frontier (t+1)

O2 Region (t+1) D E

Frontier (t) Frontier (t)

O1 Region (t) A B C

Input (Employment) Input (Employment)

The frontier-shift effect is the change of the frontier's productivity slope over the two periods (t) to (t+1), and the catch-up effect is defined by: DE AC Catch − up = DO2 AO1 Where AC and DE are the theoretical levels of employment that region O should have, in order to have the same level of productivity as the frontier, in respect of the levels of its

GDP in t and t+1. AO1 and DO2 are the levels of employment of the region O respectively in t and t+1. The productivity growth of the region.

Interpretation: If the region has reduced its productivity gap with the frontier (it has caught-up), the catch-up effect is above 1, and below 1 when the region has increased the productivity gap (it hasn’t caught-up) compared to the frontier's productivity.

Methodology to adjust GDP, total employed and unemployed at metropolitan level The proposed methodology uses the socio-economic values (GDP, employment and unemployment) in TL3 regions as data inputs (see exceptions in Annex B) and the distribution of population based on census data. In comparison to previous editions of Regions at a Glance, the methodology to adjust socio-economic data to metropolitan areas has evolved from the use of raster population data (i.e. Landscan) to municipal population census data as the input data source. This change has allowed the use of more up-to-date data (census data c.a. 2011) as well as the use of harmonised municipal boundaries over time. Indeed, long time-series have been generated using consistent boundaries of municipalities between the two census data points by using GIS techniques. The suggested methodology is composed of three main steps: ● intersect the municipal boundaries with the TL3 boundaries by the use of GIS techniques; ● attribute each municipality a GDP value by weighting for the population in each municipality; and ● calculate the sum of municipalities’ GDP values belonging to each metro area.

An improved method would be to use employment data rather than population data in step 2. For example, the United Kingdom Office for National Statistics provides income estimates at ward level down-scaling the regional values through various variables including household size, employment status, proportion of the ward population claiming social benefits, and proportion of tax payers in each of the tax bands, etc. A similar method is used by the U.S. Bureau of Economic Analysis to estimate the GDP for U.S. Metropolitan Statistical Areas. The Federal Statistical Office of Switzerland used CLC-Data-Classes urban continuous fabric, urban discontinuous fabric and industrial or commercial units for all neighbouring countries by calibrating with other data to estimate data for jobs in grid cells. However these types of data input are not available in most OECD countries therefore a simpler solution was adopted. A similar technique is applied to estimate employment and unemployment in metropolitan areas with working age population (15-65 years old) used as data input in step 2. It has to be noted that the estimates of GDP, employment and unemployment in the metropolitan areas do not adhere to international standards; the comparability among countries relies on the use of the same methodology applied to areas defined with the same criteria.

Methodology to measure the annual exposure to air pollution in regions and metropolitan areas

The estimated average exposure to air pollution (PM2.5) is based on GIS-based methodology at TL2 and metropolitan level using the satellite-based PM2.5 estimates of van Donkelaar et al. (2014) at 0.1o x0.1o geographic grid resolution. The method used to produce the estimates is the following: ● the satellite-based of air pollution at 1km2 are multiplied by the population living in that area (using a 1km2 resolution population grid); ● the exposure to air pollution in a region (or a metropolitan area) is given by the sum of 2 the population weighted values of PM2.5 in the 1km grid cells falling within the boundaries of the region (metropolitan area); and

● finally, the average exposure to PM2.5 concentration in a region is given by dividing this aggregated value by the total population in the region. This indicator is derived from global satellite observations of PM concentration. It has the advantage of being computable globally without requiring country capacity investments in data collection.

Theil entropy index Definition: Regional disparities are measured by a Theil entropy index, which is defined as: 1 N y y Theil iiln N i 1 y y th Where N is the number of regions in the OECD, yi is the variable of interest in the i- region (i.e. household income, life expectancy, homicide rate, etc.) andy is the mean of the variable of interest across all regions.

The Theil index can be easily decomposed in two components: one is the disparities within subgroups of regions – where for example is subgroup is identified by a set of regions belonging to a country; another one is the disparities between subgroups of regions (i.e. between countries). The sum of these two components is equal to the Theil index. In order to decompose the Theil index, let’s start by assuming m groups of regions (countries). The decomposition will assume the following form:

N M 1 yij yi 1 yi Theil s j ln s jln N i 11y j yMi y Where the first term of the formula is the within part of the decomposition it is equal

to the weighted average of the Theil inequality indexes of each country. Weights, si, are computed as the ratio between the country average of the variable of interest and the OECD average of the same variable. The second term is the between component of the Theil index and it represents the share of regional disparities that depends on the disparities across countries. Interpretation: The Theil index ranges between zero and , with zero representing an equal distribution and higher values representing a higher level of inequality. The index assigns equal weight to each region regardless of its size; therefore differences in the values of the index among countries may be partially due to differences in the average size of regions in each country.

ANNEX D

Responsibilities across levels of government

In federal countries, the sovereignty is shared between the federal government and federated states which have their own constitution, parliament and government, and large competences, while federal governments have in general exclusive and listed competences such as foreign policy, defence, money, criminal justice system, etc. In most federal countries, local governments are “creations” of the federated states. Falling directly under their jurisdiction, their responsibilities are defined by state constitutions and laws, and they often differ from one state to another. In quasi-federation (Spain) and “hybrid countries”, devolved nations (United Kingdom) or regions (Italy) can define, through primary and/or secondary legislative powers, the local government functioning. In unitary countries, the sovereignty is not shared. The assignment of responsibilities is generally defined by national laws. National or regional regulations provide more or less details on local governments’ responsibilities, as they often refer to the general clause of competence or “subsidiarity principle”, especially for the municipal level, which gives local authorities an explicit freedom to act in the best interests at local level. In this case, laws rarely limit and specify local responsibilities but enumerate broad functions instead, except if a particular responsibility is devolved by law to another government level. Laws can also define whether a subnational responsibility is an own/exclusive local function, a delegated task on behalf of the central government or another subnational government (SNG) or a shared responsibility with another institutional government level. In addition, some subnational responsibilities can be mandatory while others are optional. As a result, the breakdown of competences between central/federal government and SNGs as well as across SNG levels is particularly complex, leading sometimes to competing and overlapping competences and a lack of visibility and accountability concerning public policies. For each sector and sub-sector, one or more levels of government (central government, state or region, intermediary government and municipal level) may intervene and exercise one or more key functions: regulating, operating, financing and reporting (Table D.1).

Table D.1. Responsibilities sectors and sub-sectors

Responsibility sectors and sub-sectors

Social Welfare Nursery schools/Social care for children and youth/Support services for families /Elderly/Disabled people/Inclusion & poverty/Immigrants & integration of foreigners/Social welfare centres Health Primary healthcare (medical centres)/Special healthcare (e.g. dental care)/Preventative healthcare/Hygiene/Hospital Education Pre-elementary/Primary/Secondary/Higher/Vocational education/Special education/Research & Development

Table D.1. Responsibilities sectors and sub-sectors (cont.)

Responsibility sectors and sub-sectors

Utilities Refuse collection/Waste disposal/Drinking water distribution/Sewerage/Irrigation/Gas distribution/Electricity provision/Public lighting/Urban heating/Street cleaning Environment Parks & green areas/Nature preservation/Water quality/Noise/Air pollution/Soil protection Employment Subsidies/Adult vocational training/Employment services/Back to work programmes Spatial planning Urban and land use planning/Urbanism/Regional planning Housing Housing subsidies/Construction/renovation/Management Transports Road networks and facilities (highways, national, regional, local)/park spaces/Railway networks and facilities (national, regional, local)/Airports (international, national)/Ports (sea and fishing, inland waterways)/Public transport (road)/public transport (railways, tramway)/Special transport services (e.g. pupil and student transport)/Traffic signs and lights Economic development Support to local enterprises and entrepreneurship/Agriculture and rural development/Communication/IT/Industry/Technological development/ Mining/Tourism/Commerce Culture & Recreation Sports/Librairies/Museums/Cultural heritage/Media Public order and safety Police/Firefighting/Civil protection and emergency services General public administration and defence Administrative services (marriage, birth, etc.); Public facilities (town houses, etc.); Local defence Note: This classification differs from the one used in the national accounts to analyse expenditure by economic sector (Classification of the Functions of Government or COFOG). Source: Authors’ elaboration based on various sources.

Figure D.1. Breakdown of responsibilities across SNG levels: A general scheme

Municipal level Intermediary level Regional level

• A wide range of responsibilities: • Specialised and more limited • Heterogeneous and more or less – General clause of competence responsibilities of supra-municipal extensive responsibilities – Eventually, additional allocations interest depending on countries by the law (in particular, federal vs unitary) • An important role of assistance towards small municipalities • Services of regional interest: • Community services: – Secondary/higher education – Education (nursery schools, • May exercise responsibilities and professional training preelementary and primary delegated by the regions – Spatial planning education) and central government – Regional economic development – Urban planning and management and innovation – Local utility networks (water, • Responsibilities determined – Health (secondary care sewerage, waste, hygiene, etc.) by the functional level and hospitals) – Local roads and city public and the geographic area: – Social affairs, e.g. employment transport – Secondary education or services, training, inclusion, – Social affairs (support for families specialised education support to special groups, etc. and children, elderly, disabled, – Supra-municipal social and youth – Regional roads and public poverty, social benefits, etc.) welfare transport – Primary and preventative – Secondary hospitals – Culture, heritage and tourism healthcare – Waste treatment treatment – Environmental protection – Recreation (sport) and culture – Secondary roads and public – Social housing – Public order and safety (municipal transport – Public order and safety police, fire brigades) – Environment (e.g. regional police, civil – Local economic development, protection) tourism, trade fairs – Local government supervision – Environment (green areas) (in federal countries) – Social housing – Administrative and permit services

Source: Authors’ elaboration.

Subnational government responsibilities and spending power The assignment of responsibilities to SNGs does not mean that SNGs have full autonomy in exercising them. Firstly, because responsibilities can be defined as shared or delegated. Secondly, because there may be a gap between the principles and the operational reality: competent SNGs may not have the means to cope with the financial costs (unfunded mandates), or may no longer have (in case of financial crisis). Thirdly, this situation comes most of the time from the fact that SNGs do not have full autonomy and decision-making authority in their fields of responsibility, functioning sometimes more as agencies funded and regulated by the central government rather than as independent policy makers. In order to gauge true spending power, a set of institutional indicators has been established by the OECD Network on Fiscal Relations across Levels of Government, based on a detailed assessment of institutional, regulatory and administrative control central government exerts over various SNGs policy areas (Steffen Bach, Hansjörg Blöchliger and Dominik Wallau, 2009).

Figure D.2. Main categories of spending power of SNGs

Monitoring Policy & evaluation autonomy autonomy

Spending power Output Budget autonomy autonomy

Input autonomy

Five categories related to major facets of autonomy have been distinguished (Bach et al, 2009): ● Policy autonomy: To what extent do SNGs exert control over main policy objectives and main aspects of service delivery? To what extent are SNGs obliged to provide certain services e.g. through constitutional provisions or central government legislation? ● Budget autonomy: To what extent do SNGs exert control over the budget e.g. is expenditure autonomy limited by earmarked grants or expenditure limits? Do fiscal rules specifically limit fiscal autonomy in a certain policy area? ● Input autonomy: To what extent do SNGs exert control over the civil service and other input- side aspects of a service? To what extent can SNGs negotiate and shape wages and the wage structure of civil servants? To what extent are SNGs free to tender or contract out services? ● Output autonomy: To what extent do SNGs exert control over service standards such as the level and quality of public services delivered? To what extent can SNGs define output criteria? ● Monitoring and evaluation autonomy: To what extent do SNGs exert control over evaluation, monitoring and benchmarking? To which government level are service providers reporting?

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